Identifying diseases symptoms and general rules using supervised and unsupervised machine learning.

The symptoms of diseases can vary among individuals and may remain undetected in the early stages. Detecting these symptoms is crucial in the initial stage to effectively manage and treat cases of varying severity. Machine learning has made major advances in recent years, proving its effectiveness in various healthcare applications. This study aims to identify patterns of symptoms and general rules regarding symptoms among patients using supervised and unsupervised machine learning. The integration of a rule-based machine learning technique and classification methods is utilized to extend a prediction model. This study analyzes patient data that was available online through the Kaggle repository. After preprocessing the data and exploring descriptive statistics, the Apriori algorithm was applied to identify frequent symptoms and patterns in the discovered rules. Additionally, the study applied several machine learning models for predicting diseases, including stepwise regression, support vector machine, bootstrap forest, boosted trees, and neural-boosted methods. Several predictive machine learning models were applied to the dataset to predict diseases. It was discovered that the stepwise method for fitting outperformed all competitors in this study, as determined through cross-validation conducted for each model based on established criteria. Moreover, numerous significant decision rules were extracted in the study, which can streamline clinical applications without the need for additional expertise. These rules enable the prediction of relationships between symptoms and diseases, as well as between different diseases. Therefore, the results obtained in this study have the potential to improve the performance of prediction models. We can discover diseases symptoms and general rules using supervised and unsupervised machine learning for the dataset. Overall, the proposed algorithm can support not only healthcare professionals but also patients who face cost and time constraints in diagnosing and treating these diseases.

Identifying miRNA as biomarker for breast cancer subtyping using association rule.

- This paper presents a comprehensive study focused on breast cancer subtyping, utilizing a multifaceted approach that integrates feature selection, machine learning classifiers, and miRNA regulatory networks. The feature selection process begins with the CFS algorithm, followed by the Apriori algorithm for association rule generation, resulting in the identification of significant features tailored to Luminal A, Luminal B, HER-2 enriched, and Basal-like subtypes. The subsequent application of Random Forest (RF) and Support Vector Machine (SVM) classifiers yielded promising results, with the SVM model achieving an overall accuracy of 76.60 % and the RF model demonstrating robust performance at 80.85 %. Detailed accuracy metrics revealed strengths and areas for refinement, emphasizing the potential for optimizing subtype-specific recall. To explore the regulatory landscape in depth, an analysis of selected miRNAs was conducted using MIENTURNET, a tool for visualizing miRNA-target interactions. While FDR analysis raised concerns for HER-2 and Basal-like subtypes, Luminal A and Luminal B subtypes showcased significant miRNA-gene interactions. Functional enrichment analysis for Luminal A highlighted the role of Ovarian steroidogenesis, implicating specific miRNAs such as hsa-let-7c-5p and hsa-miR-125b-5p as potential diagnostic biomarkers and regulators of Luminal A breast cancer. Luminal B analysis uncovered associations with the MAPK signaling pathway, with miRNAs like hsa-miR-203a-3p and hsa-miR-19a-3p exhibiting potential diagnostic and therapeutic significance. In conclusion, this integrative approach combines machine learning techniques with miRNA analysis to provide a holistic understanding of breast cancer subtypes. The identified miRNAs and associated pathways offer insights into potential diagnostic biomarkers and therapeutic targets, contributing to the ongoing efforts to improve breast cancer diagnostics and personalized treatment strategies.

Conceptually Funded Usability Evaluation of an Application for Leveraging Descriptive Data Analysis Models for Cardiovascular Research.

The focus of this study, and the subject of this article, resides in the conceptually funded usability evaluation of an application of descriptive models to a specific dataset obtained from the East Slovak Institute of Heart and Vascular Diseases targeting cardiovascular patients. Delving into the current state-of-the-art practices, we examine the extent of cardiovascular diseases, descriptive data analysis models, and their practical applications. Most importantly, our inquiry focuses on exploration of usability, encompassing its application and evaluation methodologies, including Van Welie's layered model of usability and its inherent advantages and limitations. The primary objective of our research was to conceptualize, develop, and validate the usability of an application tailored to supporting cardiologists' research through descriptive modeling. Using the R programming language, we engineered a Shiny dashboard application named DESSFOCA (Decision Support System For Cardiologists) that is structured around three core functionalities: discovering association rules, applying clustering methods, and identifying association rules within predefined clusters. To assess the usability of DESSFOCA, we employed the System Usability Scale (SUS) and conducted a comprehensive evaluation. Additionally, we proposed an extension to Van Welie's layered model of usability, incorporating several crucial aspects deemed essential. Subsequently, we rigorously evaluated the proposed extension within the DESSFOCA application with respect to the extended usability model, drawing insightful conclusions from our findings.

The regional disparities in liver disease comorbidity among elderly Chinese based on a health ecological model: the China Health and Retirement Longitudinal Study.

PURPOSE: This study aimed to investigate the risk factors for liver disease comorbidity among older adults in eastern, central, and western China, and explored binary, ternary and quaternary co-morbid co-causal patterns of liver disease within a health ecological model. METHOD: Basic information from 9,763 older adults was analyzed using data from the China Health and Retirement Longitudinal Study (CHARLS). LASSO regression was employed to identify significant predictors in eastern, central, and western China. Patterns of liver disease comorbidity were studied using association rules, and spatial distribution was analyzed using a geographic information system. Furthermore, binary, ternary, and quaternary network diagrams were constructed to illustrate the relationships between liver disease comorbidity and co-causes. RESULTS: Among the 9,763 elderly adults studied, 536 were found to have liver disease comorbidity, with binary or ternary comorbidity being the most prevalent. Provinces with a high prevalence of liver disease comorbidity were primarily concentrated in Inner Mongolia, Sichuan, and Henan. The most common comorbidity patterns identified were "liver-heart-metabolic", "liver-kidney", "liver-lung", and "liver-stomach-arthritic". In the eastern region, important combination patterns included "liver disease-metabolic disease", "liver disease-stomach disease", and "liver disease-arthritis", with the main influencing factors being sleep duration of less than 6 h, frequent drinking, female, and daily activity capability. In the central region, common combination patterns included "liver disease-heart disease", "liver disease-metabolic disease", and "liver disease-kidney disease", with the main influencing factors being an education level of primary school or below, marriage, having medical insurance, exercise, and no disabilities. In the western region, the main comorbidity patterns were "liver disease-chronic lung disease", "liver disease-stomach disease", "liver disease-heart disease", and "liver disease-arthritis", with the main influencing factors being general or poor health satisfaction, general or poor health condition, severe pain, and no disabilities. CONCLUSION: The comorbidities associated with liver disease exhibit specific clustering patterns at both the overall and local levels. By analyzing the comorbidity patterns of liver diseases in different regions and establishing co-morbid co-causal patterns, this study offers a new perspective and scientific basis for the prevention and treatment of liver diseases.

Influence of emotions on the aggressive driving behavior of online -car-hailing drivers based on association rule mining.

Emotion is an important factor that can lead to the occurrence of aggressive driving. This paper proposes an association rule mining-based method for analysing contributing factors associated with aggressive driving behaviour among online car-hailing drivers. We collected drivers' emotion data in real time in a natural driving setting. The findings show that 29 of the top 50 association rules for aggressive driving are related to emotions, revealing a strong relationship between driver emotions and aggressive driving behaviour. The emotions of anger, surprised, happy and disgusted are frequently associated with aggressive driving behaviour. Negative emotions combined with other factors (for example, driving at high speeds and high acceleration rates and with no passengers in the vehicle) are more likely to lead to aggressive driving behaviour than negative emotions alone. The results of this study provide practical implications for the supervision and training of car-hailing drivers.

Based on the association rule mining method, we found a close connection between drivers' emotional states and the manifestation of aggressive driving behaviours. The findings indicate that the combination of negative emotions and various contributing factors significantly amplifies the likelihood of aggressive driving.

Healthcare insurance fraud detection using data mining.

BACKGROUND: Healthcare programs and insurance initiatives play a crucial role in ensuring that people have access to medical care. There are many benefits of healthcare insurance programs but fraud in healthcare continues to be a significant challenge in the insurance industry. Healthcare insurance fraud detection faces challenges from evolving and sophisticated fraud schemes that adapt to detection methods. Analyzing extensive healthcare data is hindered by complexity, data quality issues, and the need for real-time detection, while privacy concerns and false positives pose additional hurdles. The lack of standardization in coding and limited resources further complicate efforts to address fraudulent activities effectively. METHODOLGY: In this study, a fraud detection methodology is presented that utilizes association rule mining augmented with unsupervised learning techniques to detect healthcare insurance fraud. Dataset from the Centres for Medicare and Medicaid Services (CMS) 2008-2010 DE-SynPUF is used for analysis. The proposed methodology works in two stages. First, association rule mining is used to extract frequent rules from the transactions based on patient, service and service provider features. Second, the extracted rules are passed to unsupervised classifiers, such as IF, CBLOF, ECOD, and OCSVM, to identify fraudulent activity. RESULTS: Descriptive analysis shows patterns and trends in the data revealing interesting relationship among diagnosis codes, procedure codes and the physicians. The baseline anomaly detection algorithms generated results in 902.24 seconds. Another experiment retrieved frequent rules using association rule mining with apriori algorithm combined with unsupervised techniques in 868.18 seconds. The silhouette scoring method calculated the efficacy of four different anomaly detection techniques showing CBLOF with highest score of 0.114 followed by isolation forest with the score of 0.103. The ECOD and OCSVM techniques have lower scores of 0.063 and 0.060, respectively. CONCLUSION: The proposed methodology enhances healthcare insurance fraud detection by using association rule mining for pattern discovery and unsupervised classifiers for effective anomaly detection.

Factors influencing illegal dumping of hazardous waste in China.

In recent years, illegal dumping of hazardous waste (IDHW) in China has become a recurring problem. Effective identification and exploration of the factors influencing illegal dumping are crucial for incident prevention and hazardous waste management, but its analysis has rarely been reported. Thus, this study focused on 568 cases of IDHW officially reported by the government. Through regular expressions, the categories of dumped wastes and the provinces where the incidents occurred were extracted. Furthermore, a comprehensive set of influencing factors was constructed by text mining for the case content and by the integration from the existing literature. On this basis, the unstructured and structured data were integrated using a Boolean dataset to respectively explore the association rules of influencing factors for the overall IDHW and for major waste categories, in conjunction with the extracted province information. Subsequently, a Bayesian network was constructed by utilizing the results of association rules mining and the key factors were identified through corresponding analysis. The findings of this study reveal a close connection between various influencing factors, with distinct key factors identified for different categories of hazardous waste. Among them, law-enforcement emerges as a crucial factor in most IDHW cases, while the factor of public monitoring for metallic hazardous waste and the factor of government supervision for distillation residue waste and other waste play a key role in their respective cases of illegal dumping. These findings offer a fresh research perspective for investigating the factors influencing IDHW and present helpful insights for developing effective strategies to prevent and control such incidents.

Automated mass screening and association rules analysis for comorbidities of psoriasis: A population-based case-control study.

Patients with psoriasis frequently have comorbidities, which are linked to higher mortality rates. An in-depth investigation of comorbidities and their effects on health can help improve the management of patients with psoriasis. We conducted a comprehensive and unbiased investigation of comorbidities in patients with psoriasis and explored the pattern of association between comorbidities. A nationwide population-based study included 384 914 patients with psoriasis and 384 914 matched controls between 2011 and 2021. We used automated mass screening of all diagnostic codes to identify psoriasis-associated comorbidities and applied association rule analysis to explore the patterns of comorbidity associations in patients with psoriasis. Patients with psoriasis had an increased risk of autoimmunity-related diseases such as inflammatory arthritis, Crohn's disease, type 1 diabetes, and acute myocardial infarction. The comorbidities of patients with psoriasis with a history of cardiovascular events demonstrated strong interrelationships with other cardiovascular risk factors including type 2 diabetes mellitus, essential hypertension, and dyslipidemia. We also found comorbidities, such as malignant skin tumors and kidney and liver diseases, which could have adverse effects of anti-psoriasis therapy. In contrast, patients with psoriasis showed a decreased association with upper respiratory tract infection. Our results imply that comorbidities in patients with psoriasis are associated with the systemic inflammation of psoriasis and the detrimental effects of its treatment. Furthermore, we found patterns of associations between the cardiovascular risk factors and psoriasis. Mass screening and association analyses using large-scale databases can be used to investigate impartially the comorbidities of psoriasis and other diseases.

Exploring the use of association rules in random forest for predicting heart disease.

Heart disease is one of the most dangerous diseases in the world. People with these diseases, most of them end up losing their lives. Therefore, machine learning algorithms have proven to be useful in this sense to help decision-making and prediction from the large amount of data generated by the healthcare sector. In this work, we have proposed a novel method that allows increasing the performance of the classical random forest technique so that this technique can be used for the prediction of heart disease with its better performance. We used in this study other classifiers such as classical random forest, support vector machine, decision tree, Naïve Bayes, and XGBoost. This work was done in the heart dataset Cleveland. According to the experimental results, the accuracy of the proposed model is better than that of other classifiers with 83.5%.This study contributed to the optimization of the random forest technique as well as gave solid knowledge of the formation of this technique.

Analysis of pre-crash scenarios and contributing factors for autonomous vehicle crashes at intersections.

Intersections are high-risk locations for autonomous vehicles (AVs). Crash causation analysis based on pre-crash scenarios can provide new insight into these crashes that can lead to effective countermeasures, but there are significant differences in pre-crash scenarios between autonomous and conventional vehicles, and inadequate AV data has put limits on research. The association rule method, however, can yield useful results despite these limits. This study therefore aims to use the method with pre-crash scenarios to understand the characteristics and contributing factors of AV crashes at intersections from the latest 5-year AV crash data. Analysis of 197 AV crashes at intersections revealed 30 types of pre-crash scenarios. The rear-end crash (58.88%) and lane change crash (16.24%) were the most frequently occurring scenarios for AVs. The proportion of AVs being rear-ended by conventional vehicles was 58.38%. The main contributing factors of these two most common AV scenarios were identified by association rules and crash causes were analyzed from the perspective of AV decision-making. The main factors contributing to the AV rear-end scenario were location outside the intersection in the intersection-related area, traffic signal control, autonomous engaged mode, mixed-use or public land, and weekdays, while those for lane change scenarios were on-street parking and the time of 8:00 a.m. Important causes of rear-end crashes attributable to the AV were inadequate stop and deceleration decisions by the AV's automated driving system (ADS) and insufficient collision avoidance decisions in lane change crashes. Identification of the pre-crash characteristics and contributing factors provide new insight into AV crash causation and can be used in the determination of the AV's operational design domain and the development and optimization of the AV's ADS at intersections. These findings can also play a role in guiding traffic safety agencies to discover AV hotspots and propose AV management regulations.

Synergistic horizontal transfer of antibiotic resistance genes and transposons in the infant gut microbial genome.

Transposons, plasmids, bacteriophages, and other mobile genetic elements facilitate horizontal gene transfer in the gut microbiota, allowing some pathogenic bacteria to acquire antibiotic resistance genes (ARGs). Currently, the relationship between specific ARGs and specific transposons in the comprehensive infant gut microbiome has not been elucidated. In this study, ARGs and transposons were annotated from the Unified Human Gastrointestinal Genome (UHGG) and the Early-Life Gut Genomes (ELGG). Association rules mining was used to explore the association between specific ARGs and specific transposons in UHGG, and the robustness of the association rules was validated using the external database in ELGG. Our results suggested that ARGs and transposons were more likely to be relevant in infant gut microbiota compared to adult gut microbiota, and nine robust association rules were identified, among which Klebsiella pneumoniae, Enterobacter hormaechei_A, and Escherichia coli_D played important roles in this association phenomenon. The emphasis of this study is to investigate the synergistic transfer of specific ARGs and specific transposons in the infant gut microbiota, which can contribute to the study of microbial pathogenesis and the ARG dissemination dynamics.IMPORTANCEThe transfer of transposons carrying antibiotic resistance genes (ARGs) among microorganisms accelerates antibiotic resistance dissemination among infant gut microbiota. Nonetheless, it is unclear what the relationship between specific ARGs and specific transposons within the infant gut microbiota. K. pneumoniae, E. hormaechei_A, and E. coli_D were identified as key players in the nine robust association rules we discovered. Meanwhile, we found that infant gut microorganisms were more susceptible to horizontal gene transfer events about specific ARGs and specific transposons than adult gut microorganisms. These discoveries could enhance the understanding of microbial pathogenesis and the ARG dissemination dynamics within the infant gut microbiota.

Mining association rules between the granulation feasibility and physicochemical properties of aqueous extracts from Chinese herbal medicine in fluidized bed granulation.

Fluidized bed granulation (FBG) is a widely used granulation technology in the pharmaceutical industry. However, defluidization caused by the formation of large aggregates poses a challenge to FBG, particularly in traditional Chinese medicine (TCM) due to its complex physicochemical properties of aqueous extracts. Therefore, this study aims to identify the complex relationships between physicochemical characteristics and defluidization using data mining methods. Initially, 50 types of TCM were decocted and assessed for their potential influence on defluidization using a set of 11 physical properties and 10 chemical components, utilizing the loss rate as an evaluation index. Subsequently, the random forest (RF) and Apriori algorithms were utilized to uncover intricate association rules among physicochemical characteristics and defluidization. The RF algorithm analysis revealed the top 8 critical factors associated with defluidization. These factors include physical properties like glass transition temperature (Tg) and dynamic surface tension (DST) of DST100ms, DST1000ms, DST10ms and conductivity, in addition to chemical components such as fructose, glucose and protein contents. The results from Apriori algorithm demonstrated that lower Tg and conductivity were associated with an increased risk of defluidization, resulting in a higher loss rate. Moreover, DST100ms, DST1000ms and DST10ms exhibited a contrasting trend in the physical properties Specifically, defluidization probability increases when Tg and conductivity dip below 29.04℃ and 6.21 ms/m respectively, coupled with DST10ms, DST100ms and DST1000ms values exceeding 70.40 mN/m, 66.66 mN/m and 61.58 mN/m, respectively. Moreover, an elevated content of low molecular weight saccharides was associated with a higher occurrence of defluidization, accompanied by an increased loss rate. In contrast, protein content displayed an opposite trend regarding chemical properties. Precisely, the defluidization likelihood amplifies when fructose and glucose contents surpass 20.35 mg/g and 34.05 mg/g respectively, and protein concentration is less than 1.63 mg/g. Finally, evaluation criteria for defluidization were proposed based on these results, which could be used to avoid this situation during the granulation process. This study demonstrated that the RF and Apriori algorithms are effective data mining methods capable of uncovering key factors affecting defluidization.

Comorbidities in congenital heart disease: different patterns in childhood and adulthood.

BACKGROUND: Existing studies were no exploration of the association between congenital heart disease (CHD) in children and comorbidities. This study was to assess the prevalence and number of comorbidities in CHD among children and adults, and to compare the comorbidity patterns by children and adults using association rule analysis. METHODS: Patients identified by the International Classification of Diseases, Ninth Revision (ICD-9) code in the Medical Information Mart for Intensive Care III (MIMIC-III) 2001-2012 and MIMIC-IV 2008-2018 were included in this cross-sectional study. Association rule analysis was used to explore associations between CHD and comorbidities in children and adults using values of support (%), confidence (%), and lift. RESULTS: Among 60,400 eligible patients, 1.54% of adults had CHD and 0.83% of adults had CHD with at least one comorbidity, 13.79% had CHD and 12.37% had CHD with at least one comorbidity in children. The most common comorbidities were circulatory system diseases (53.78%), endocrine diseases (35.76%), and respiratory system diseases (23.46%) in adults with CHD, and the most common comorbidities were perinatal diseases (87.50%) in children with CHD. The comorbidity rate was 90.19% and 56.68% in children and adults, respectively. In children, perinatal diseases, circulatory system diseases, and endocrine diseases had the highest prevalence. The incidence of circulatory system diseases, perinatal diseases and endocrine diseases in CHD adults was confidence = 31.56%, 36.11%, and 23.23%, respectively. Perinatal diseases were common comorbidities among all CHD severity groups in children and adults. CONCLUSION: The prevalence of comorbidities in children with CHD was higher than that in adults with CHD. The most common comorbidities were perinatal diseases and endocrine diseases among children and adults with CHD, respectively. Our study provided insights into comorbidity patterns in children and adults with CHD.

Acquisition of temporal patterns from electronic health records: an application to multimorbid patients.

BACKGROUND: The exponential growth of digital healthcare data is fueling the development of Knowledge Discovery in Databases (KDD). Extracting temporal relationships between medical events is essential to reveal hidden patterns that can help physicians find optimal treatments, diagnose illnesses, detect drug adverse reactions, and more. This paper presents an approach for the extraction of patient evolution patterns from electronic health records written in Catalan and/or Spanish. METHODS: We propose a robust formulation for extracting Temporal Association Rules (TARs) that goes beyond simple rule extraction by considering the sequence of multiple visits. Our highly configurable algorithm leverages this formulation to extract Temporal Association Rules from sequences of medical instances. We can generate rules in the desired format, content, and temporal factors while accounting for different levels of abstraction of medical instances. To demonstrate the effectiveness of our methodology, we applied it to extract patient evolution patterns from clinical histories of multimorbid patients suffering from heart disease and stroke who visited Primary Care Centers (CAP) in Catalonia. Our main objective is to uncover complex rules with multiple temporal steps, that comprise a set of medical instances. RESULTS: As we are working with real-world, error-prone data, we propose a process of validation of the results by expert practitioners in primary care. Despite our limited dataset, the high percentage of patterns deemed correct and relevant by the experts is promising. The insights gained from these patterns can inform preventive measures and help detect risk factors, ultimately leading to better treatments and outcomes for patients. CONCLUSION: Our algorithm successfully extracted a set of meaningful and relevant temporal patterns, especially for the specific type of multimorbid patients considered. These patterns were evaluated by experts and demonstrated the ability to predict risk factors that are commonly associated with certain diseases. Moreover, the average time gap between the occurrence of medical events provided critical insight into the term of these risk factors. This information holds significant value in the context of primary healthcare and preventive medicine, highlighting the potential of our method to serve as a valuable medical tool.

The lower He-sea points playing a significant role in postoperative ileus in colorectal cancer treated with acupuncture: based on machine-learning.

Background: Postoperative ileus (POI) is a common complication following abdominal surgery, which can lead to significant negative impacts on patients' well-being and healthcare costs. However, the efficacy of current treatments is not satisfactory. The purpose of this study was to evaluate the therapeutic effects of acupuncture intervention and explore the regulation of acupoint selection for treating POI in colorectal cancer (CRC) patients. Methods: We searched eight electronic databases to identify randomized controlled trials (RCTs) on acupuncture for POI in CRC and conducted a meta-analysis. Subsequently, we utilized the Apriori algorithm and the Frequent pattern growth algorithm, in conjunction with complex network and cluster analysis, to identify association rules of acupoints. Results: The meta-analysis showed that acupuncture led to significant reductions in time to first defecation (MD=-20.93, 95%CI: -25.35, -16.51; I2 = 93.0%; p < 0.01; n=2805), first flatus (MD=-15.08, 95%CI: -18.39, -11.76; I2 = 96%; p < 0.01; n=3284), and bowel sounds recovery (MD=-10.96, 95%CI: -14.20, -7.72; I2 = 94%; p < 0.01; n=2043). A subgroup analysis revealed that acupuncture not only reduced the duration of POI when administered alongside conventional care but also further expedited the recovery of gut function after colorectal surgery when integrated into the enhanced recovery after surgery (ERAS) pathway. The studies included in the analysis reported no instances of serious adverse events associated with acupuncture. We identified Zusanli (ST36), Shangjuxu (ST37), Neiguan (PC6), Sanyinjiao (SP6), Xiajuxu (ST39), Hegu (LI4), Tianshu (ST25), and Zhongwan (RN12) as primary acupoints for treating POI. Association rule mining suggested potential acupoint combinations including {ST37, ST39}≥{ST36}, {PC6, ST37}≥{ST36}, {SP6, ST37}≥{ST36}, and {ST25, ST37}≥{ST36}. Conclusion: Meta-analysis indicates acupuncture's safety and superior effectiveness over postoperative care alone in facilitating gastrointestinal recovery. Machine-learning approaches highlight the importance of the lower He-sea points, including Zusanli (ST36) and Shangjuxu (ST37), in treating POI in CRC patients. Incorporating additional acupoints such as Neiguan (PC6) (for pain and vomiting) and Sanyinjiao (SP6) (for abdominal distension and poor appetite) can optimize treatment outcomes. These findings offer valuable insights for refining treatment protocols in both clinical and experimental settings, ultimately enhancing patient care.

Semantic Retrieval of Remote Sensing Images Based on the Bag-of-Words Association Mapping Method.

With the increasing demand for remote sensing image applications, extracting the required images from a huge set of remote sensing images has become a hot topic. The previous retrieval methods cannot guarantee the efficiency, accuracy, and interpretability in the retrieval process. Therefore, we propose a bag-of-words association mapping method that can explain the semantic derivation process of remote sensing images. The method constructs associations between low-level features and high-level semantics through visual feature word packets. An improved FP-Growth method is proposed to achieve the construction of strong association rules to semantics. A feedback mechanism is established to improve the accuracy of subsequent retrievals by reducing the semantic probability of incorrect retrieval results. The public datasets AID and NWPU-RESISC45 were used to validate these experiments. The experimental results show that the average accuracies of the two datasets reach 87.5% and 90.8%, which are 22.5% and 20.3% higher than VGG16, and 17.6% and 15.6% higher than ResNet18, respectively. The experimental results were able to validate the effectiveness of our proposed method.

Phytochemistry, data mining, pharmacology, toxicology and the analytical methods of Cyperus rotundus L. (Cyperaceae): a comprehensive review.

Cyperus rotundus L. has been widely used in the treatment and prevention of numerous diseases in traditional systems of medicine around the world, such as nervous, gastrointestinal systems diseases and inflammation. In traditional Chinese medicine (TCM), its rhizomes are frequently used to treat liver disease, stomach pain, breast tenderness, dysmenorrheal and menstrual irregularities. The review is conducted to summarize comprehensively the plant's vernacular names, distribution, phytochemistry, pharmacology, toxicology and analytical methods, along with the data mining for TCM prescriptions containing C. rotundus. Herein, 552 compounds isolated or identified from C. rotundus were systematically collated and classified, concerning monoterpenoids, sesquiterpenoids, flavonoids, phenylpropanoids, phenolics and phenolic glycosides, triterpenoids and steroids, diterpenoids, quinonoids, alkaloids, saccharides and others. Their pharmacological effects on the digestive system, nervous system, gynecological diseases, and other bioactivities like antioxidant, anti-inflammatory, anti-cancer, insect repellent, anti-microbial activity, etc. were summarized accordingly. Moreover, except for the data mining on the compatibility of C. rotundus in TCM, the separation, identification and analytical methods of C. rotundus compositions were also systematically summarized, and constituents of the essential oils from different regions were re-analyzed using multivariate statistical analysis. In addition, the toxicological study progresses on C. rotundus revealed the safety property of this herb. This review is designed to serve as a scientific basis and theoretical reference for further exploration into the clinical use and scientific research of C. rotundus. Supplementary Information: The online version contains supplementary materials available at 10.1007/s11101-023-09870-3.

Identification of status quo and association rules for chronic comorbidity among Chinese middle-aged and older adults rural residents.

Background: Chronic comorbidity has become a major challenge in chronic disease prevention and control. This issue is particularly pronounced in rural areas of developing countries, where the prevalence of chronic disease comorbidity is high, especially among middle-aged and older adults populations. However, the health status of middle-aged and older adults individuals in rural areas of China has received inadequate attention. Therefore, it is crucial to investigate the correlation among chronic diseases to establish a reference basis for adjusting health policies aimed at promoting the prevention and management of chronic diseases among middle-aged and older adults individuals. Methods: This study selected 2,262 middle-aged and older adults residents aged 50 years or older in Shangang Village, Jiangsu Province, China, as the study population. To analyze the chronic comorbidity of middle-aged and older adults residents with different characteristics, we used the χ2 test with SPSS statistical software. Data analysis was conducted using the Apriori algorithm of Python software, set to mine the strong association rules of positive correlation between chronic disease comorbidities of middle-aged and older adults residents. Results: The prevalence of chronic comorbidity was 56.6%. The chronic disease comorbidity group with the highest prevalence rate was the lumbar osteopenia + hypertension group. There were significant differences in the prevalence of chronic disease comorbidity among middle-aged and older adults residents in terms of gender, BMI, and chronic disease management. The Apriori algorithm was used to screen 15 association rules for the whole population, 11 for genders, and 15 for age groups. According to the order of support, the most common association rules of comorbidity of three chronic diseases were: {lumbar osteopenia} → {hypertension} (support: 29.22%, confidence: 58.44%), {dyslipidemia} → {hypertension} (support: 19.14%, confidence: 65.91%) and {fatty liver} → {hypertension} (support: 17.82%, confidence: 64.17%). Conclusion: The prevalence of chronic comorbidity among middle-aged and older adults rural residents in China is relatively high. We identified many association rules among chronic diseases, dyslipidemia is mostly the antecedent, and hypertension is primarily the result. In particular, the majority of comorbidity aggregation patterns consisted of hypertension and dyslipidemia. By implementing scientifically-proven prevention and control strategies, the development of healthy aging can be promoted.

Analysis of multiple chronic disease characteristics in middle-aged and elderly South Koreans by exercise habits based on association rules mining algorithm.

BACKGROUND: The term, "multiple chronic diseases" (MCD), describes a patient with two or more chronic conditions simultaneously at the same time. Compared with general chronic diseases, it is linked to poorer health outcomes, more difficult clinical management, and higher medical expenses. Several existing MCD guidelines support a healthy lifestyle including regular physical activities but do not include specific exercise therapy recommendations. This study aimed to understand the prevalence and model of MCD in middle-aged and elderly South Koreans by comparing MCD characteristics with exercise habits, to provide a theoretical basis for the implementation of exercise therapy in these patients. METHODS: The data of 8477 participants aged > 45 years from the "2020 Korean Health Panel Survey" were used to analyze the current status of MCD in the middle-aged and elderly. The Chi-square test for categorical variables and the t-test for continuous variables. the used software was IBM SPSS Statistics 26.0 and IBM SPSS Modeler 18.0. RESULTS: In this study, the morbidity rate of MCD was 39.1%. Those with MCD were more likely to be female (p < 0.001), seniors over 65 years of age (p < 0.001), with low education level, no regular exercise behavior (p < 0.01). Chronic renal failure (93.9%), depression (90.4%), and cerebrovascular disease (89.6%) were the top three diseases identified in patients with MCD. A total of 37 association rules were identified for the group of individuals who did not engage in regular exercise. This equated to 61% more than that of the regular exercise group, who showed only 23 association rules. In the extra association rules, cardiovascular diseases (150%), spondylosis (143%), and diabetes (125%) are the three chronic diseases with the highest frequency increase. CONCLUSIONS: Association rule analysis is effective in studying the relationship between various chronic diseases in patients with MCD. It also effectively helps with the identification of chronic diseases that are more sensitive to regular exercise behaviors. The findings from this study may be used to formulate more appropriate and scientific exercise therapy for patients with MCD.

Short-term relation between air pollutants and hospitalizations for respiratory diseases: analysis by temporal association rules.

This study investigates the relation between exposure to critical air pollution events with multipollutant (CO, PM10, PM2.5, NO2, O3, and SO2) and hospitalizations for respiratory diseases in the metropolitan area of São Paulo (RMSP) and in the countryside and coastline, from 2017 to 2021. Data mining analysis by temporal association rules searched for frequent patterns of respiratory diseases and multipollutants associated with time intervals. In the results, pollutants PM10, PM2.5, and O3 showed high concentration values in the three regions, SO2 on the coast, and NO2 in the RMSP. Seasonality was similar between pollutants and between cities and concentrations significantly higher in winter, except for O3, which was present in warm seasons. Hospitalizations were recurrent during the transition from summer to colder periods. In approximately 35% of the total days with hospitalization greater than the annual average, one or more pollutants had a high concentration. The rules showed that PM2.5, PM10, and O3 pollutants are strongly associated with increased hospitalizations in the RMSP (PM2.5 and PM10 with 38.5% support and 77% confidence) and in Campinas (PM2.5 with 66.1% support and 94% confidence) and the pollutant O3 with maximum support of 17.5%. On the coast, SO2 was related to high hospitalizations (43.85% support and 80% confidence). The pollutants CO and NO2 were not associated with the increase in hospitalizations. The ratio delay indicates the pollutants that were associated with hospitalizations, having concentration remained above the limit for three days, oscillating in smaller hospitalizations on the 1st day and again higher on the 2nd and 3rd days of delay, in a decreasing way. In conclusion, high pollutant exposure is significantly associated with daily hospitalization for respiratory problems. The cumulative effect of air pollutants increased hospitalization in the following days, in addition to identifying the pollutants and which pollutant combinations are most harmful to health in each region.