Exploring the Health Literacy Behavior Patterns of Male Patients Using an Interpretable Method.

Background: Improving overall and individual health literacy is a major focus of national initiatives in China and similar initiatives globally. However, few studies have examined the identification and improvement of individual health literacy levels, especially among patients. Purpose: To develop an interpretable method with decision rules to assess the health literacy levels of male patients and identify key factors influencing health literacy levels. Methods: Using a convenience sampling method, we conducted on-site surveys with 212 male patients of a hospital in China from July 2020 to September 2020. The questionnaire was developed by the Ministry of Health of the People's Republic of China. A total of 206 of the completed surveys were ultimately included for analyses in this study. The rough set theory was used to identify conditional attributes (ie, key factors) and decision attributes (ie, levels of health literacy) and to establish decision rules between them. These rules specifically describe how different combinations of conditional attributes can affect health literacy levels among men. Results: Basic knowledge, concepts, and health skills are important in identifying whether male patients have health literacy. Health skills, scientific health concepts, healthy lifestyles and behaviors, infectious disease prevention and control literacy, basic medical literacy, and health information literacy can be identified as cognitive behaviors with varying degrees of health literacy among patients. Conclusion: This model can effectively identify the key factors and decision rules for male patients' health literacy. Simultaneously, it can be applied to clinical nursing practice, making it easier for hospitals to guide male patients to improve their health literacy.

How to Cure Alzheimer's Disease.

There has been a lot of buzz surrounding new drug discoveries that claim to cure Alzheimer's disease (AD). However, it is crucial to keep in mind that the changes in the brain linked to AD start occurring 20-30 years before the first symptoms arise. By the time symptoms become apparent, many areas of the brain have already been affected. That's why experts are focusing on identifying the onset of the neurodegeneration processes to prevent or cure AD effectively. Scientists use biomarkers and machine learning methods to analyze AD progressions and estimate them "backward" in time to discover the beginning of the disease.

An Optimal Hierarchical Approach for Oral Cancer Diagnosis Using Rough Set Theory and an Amended Version of the Competitive Search Algorithm.

Oral cancer is introduced as the uncontrolled cells' growth that causes destruction and damage to nearby tissues. This occurs when a sore or lump grows in the mouth that does not disappear. Cancers of the cheeks, lips, floor of the mouth, tongue, sinuses, hard and soft palate, and lungs (throat) are types of this cancer that will be deadly if not detected and cured in the beginning stages. The present study proposes a new pipeline procedure for providing an efficient diagnosis system for oral cancer images. In this procedure, after preprocessing and segmenting the area of interest of the inputted images, the useful characteristics are achieved. Then, some number of useful features are selected, and the others are removed to simplify the method complexity. Finally, the selected features move into a support vector machine (SVM) to classify the images by selected characteristics. The feature selection and classification steps are optimized by an amended version of the competitive search optimizer. The technique is finally implemented on the Oral Cancer (Lips and Tongue) images (OCI) dataset, and its achievements are confirmed by the comparison of it with some other latest techniques, which are weight balancing, a support vector machine, a gray-level co-occurrence matrix (GLCM), the deep method, transfer learning, mobile microscopy, and quadratic discriminant analysis. The simulation results were authenticated by four indicators and indicated the suggested method's efficiency in relation to the others in diagnosing the oral cancer cases.

Uncover the reasons for performance differences between measurement functions (Provably).

Recently, an exciting experimental conclusion in Li et al. (Knowl Inf Syst 62(2):611-637, 1) about measures of uncertainty for knowledge bases has attracted great research interest for many scholars. However, these efforts lack solid theoretical interpretations for the experimental conclusion. The main limitation of their research is that the final experimental conclusions are only derived from experiments on three datasets, which makes it still unknown whether the conclusion is universal. In our work, we first review the mathematical theories, definitions, and tools for measuring the uncertainty of knowledge bases. Then, we provide a series of rigorous theoretical proofs to reveal the reasons for the superiority of using the knowledge amount of knowledge structure to measure the uncertainty of the knowledge bases. Combining with experiment results, we verify that knowledge amount has much better performance for measuring uncertainty of knowledge bases. Hence, we prove an empirical conclusion established through experiments from a mathematical point of view. In addition, we find that for some knowledge bases that cannot be classified by entity attributes, such as ProBase (a probabilistic taxonomy), our conclusion is still applicable. Therefore, our conclusions have a certain degree of universality and interpretability and provide a theoretical basis for measuring the uncertainty of many different types of knowledge bases, and the findings of this study have a number of important implications for future practice.

Development of a Rice Plant Disease Classification Model in Big Data Environment.

More than the half of the global population consume rice as their primary energy source. Therefore, this work focused on the development of a prediction model to minimize agricultural loss in the paddy field. Initially, rice plant diseases, along with their images, were captured. Then, a big data framework was used to encounter a large dataset. In this work, at first, feature extraction process is applied on the data and after that feature selection is also applied to obtain the reduced data with important features which is used as the input to the classification model. For the rice disease datasets, features based on color, shape, position, and texture are extracted from the infected rice plant images and a rough set theory-based feature selection method is used for the feature selection job. For the classification task, ensemble classification methods have been implemented in a map reduce framework for the development of the efficient disease prediction model. The results on the collected disease data show the efficiency of the proposed model.

Pruning Decision Rules by Reduct-Based Weighting and Ranking of Features.

Methods and techniques of feature selection support expert domain knowledge in the search for attributes, which are the most important for a task. These approaches can also be used in the process of closer tailoring of the obtained solutions when dimensionality reduction is aimed not only at variables but also at learners. The paper reports on research where attribute rankings were employed to filter induced decision rules. The rankings were constructed through the proposed weighting factor based on the concept of decision reducts-a feature reduction mechanism embedded in the rough set theory. Classical rough sets operate only in discrete input space by indiscernibility relation. Replacing it with dominance enables processing real-valued data. Decision reducts were found for both numeric and discrete attributes, transformed by selected discretisation approaches. The calculated ranking scores were used to control the selection of decision rules. The performance of the resulting rule classifiers was observed for the entire range of rejected variables, for decision rules with conditions on continuous values, discretised conditions, and also inferred from discrete data. The predictive powers were analysed and compared to detect existing trends. The experiments show that for all variants of the rule sets, not only was dimensionality reduction possible, but also predictions were improved, which validated the proposed methodology.

Managing Boundary Uncertainty in Diagnosing the Patients of Rural Area Using Fuzzy and Rough Set.

People of rural India often suffer from acute health conditions like diarrhea, flu, lung congestion, and anemia, but they are not receiving treatment even at primary level due to scarcity of doctors and health infrastructure in remote villages. Health workers are involved in diagnosing the patients based on the symptoms and physiological signs. However, due to inadequate domain knowledge, lack of expertise, and error in measuring the health data, uncertainty creeps in the decision space, resulting many false cases in predicting the diseases. The paper proposes an uncertainty management technique using fuzzy and rough set theory to diagnose the patients with minimum false-positive and false-negative cases. We use fuzzy variables with proper semantic to represent the vagueness of input data, appearing due to measurement error. We derive initial degree of belonging of each patient in two different disease class labels (YES/NO) using the fuzzified input data. Next, we apply rough set theory to manage uncertainty in diagnosing the diseases by learning approximations of the decision boundary between the two class labels. The optimum lower and upper approximation membership functions for each disease class label have been obtained using Non-dominated Sorting Genetic Algorithm-II (NSGA-II). Finally, using the proposed disease_similarity_factor, new patients are diagnosed precisely with 98% accuracy and minimum false cases.

Seismic vulnerability assessment of urban buildings using the rough set theory and weighted linear combination.

Seismic vulnerability assessment of urban buildings is among the most crucial procedures to post-disaster response and recovery of infrastructure systems. The present study proceeds to estimate the seismic vulnerability of urban buildings and proposes a new framework training on the two objectives. First, a comprehensive interpretation of the effective parameters of this phenomenon including physical and human factors is done. Second, the Rough Set theory is used to reduce the integration uncertainties, as there are numerous quantitative and qualitative data. Both objectives were conducted on seven distinct earthquake scenarios with different intensities based on distance from the fault line and the epicenter. The proposed method was implemented by measuring seismic vulnerability for the seven specified seismic scenarios. The final results indicated that among the entire studied buildings, 71.5% were highly vulnerable as concerning the highest earthquake scenario (intensity=7MM and acceleration calculated based on the epicenter), while in the lowest earthquake scenario (intensity=5MM), the percentage of vulnerable buildings decreased to approximately 57%. Also, the findings proved that the distance from the fault line rather than the earthquake center (epicenter) has a significant effect on the seismic vulnerability of urban buildings. The model was evaluated by comparing the results with the weighted linear combination (WLC) method. The accuracy of the proposed model was substantiated according to evaluation reports. Vulnerability assessment based on the distance from the epicenter and its comparison with the distance from the fault shows significant reliable results.

On the Depth of Decision Trees with Hypotheses.

In this paper, based on the results of rough set theory, test theory, and exact learning, we investigate decision trees over infinite sets of binary attributes represented as infinite binary information systems. We define the notion of a problem over an information system and study three functions of the Shannon type, which characterize the dependence in the worst case of the minimum depth of a decision tree solving a problem on the number of attributes in the problem description. The considered three functions correspond to (i) decision trees using attributes, (ii) decision trees using hypotheses (an analog of equivalence queries from exact learning), and (iii) decision trees using both attributes and hypotheses. The first function has two possible types of behavior: logarithmic and linear (this result follows from more general results published by the author earlier). The second and the third functions have three possible types of behavior: constant, logarithmic, and linear (these results were published by the author earlier without proofs that are given in the present paper). Based on the obtained results, we divided the set of all infinite binary information systems into four complexity classes. In each class, the type of behavior for each of the considered three functions does not change.

Application of Rough Set Theory to Improve Outpatient Medical Service Quality in Public Hospitals Based on the Patient Perspective.

Purpose: To analyze the key factors and decision-making behaviors affecting overall satisfaction based on perceptual data of outpatients. Methods: The official satisfaction questionnaire developed by the National Health Commission of the People's Republic of China was used. Rough set theory was used to identify the perception patterns between condition attributes (i.e., service factors) and a decision attribute (i.e., overall service level) and to express them in rule form (i.e., if-then). Results: The four minimal-coverage rules, with strength exceeding 10% in the good class, and six crucial condition attributes were obtained: "Ease of registration (C1)," "Respected by registered staff (C2)," "Registered staff's listening (C3)," "Respected by doctor (C9)," "Signpost (C12)," and "Privacy (C16)." In addition, the average hit rate for 5-fold cross-validation was 90.86%. Conclusions: A series of decision rules could help decision-makers easily understand outpatients' situations and propose more suitable programs for improving hospital service quality because these decision rules are based on actual outpatient experiences.

Combining genetic algorithm with machine learning strategies for designing potent antimicrobial peptides.

BACKGROUND: Current methods in machine learning provide approaches for solving challenging, multiple constraint design problems. While deep learning and related neural networking methods have state-of-the-art performance, their vulnerability in decision making processes leading to irrational outcomes is a major concern for their implementation. With the rising antibiotic resistance, antimicrobial peptides (AMPs) have increasingly gained attention as novel therapeutic agents. This challenging design problem requires peptides which meet the multiple constraints of limiting drug-resistance in bacteria, preventing secondary infections from imbalanced microbial flora, and avoiding immune system suppression. AMPs offer a promising, bioinspired design space to targeting antimicrobial activity, but their versatility also requires the curated selection from a combinatorial sequence space. This space is too large for brute-force methods or currently known rational design approaches outside of machine learning. While there has been progress in using the design space to more effectively target AMP activity, a widely applicable approach has been elusive. The lack of transparency in machine learning has limited the advancement of scientific knowledge of how AMPs are related among each other, and the lack of general applicability for fully rational approaches has limited a broader understanding of the design space. METHODS: Here we combined an evolutionary method with rough set theory, a transparent machine learning approach, for designing antimicrobial peptides (AMPs). Our method achieves the customization of AMPs using supervised learning boundaries. Our system employs in vitro bacterial assays to measure fitness, codon-representation of peptides to gain flexibility of sequence selection in DNA-space with a genetic algorithm and machine learning to further accelerate the process. RESULTS: We use supervised machine learning and a genetic algorithm to find a peptide active against S. epidermidis, a common bacterial strain for implant infections, with an improved aggregation propensity average for an improved ease of synthesis. CONCLUSIONS: Our results demonstrate that AMP design can be customized to maintain activity and simplify production. To our knowledge, this is the first time when codon-based genetic algorithms combined with rough set theory methods is used for computational search on peptide sequences.

Real-time contamination zoning in water distribution networks for contamination emergencies: a case study.

Contamination of urban water distribution systems (WDS) is a critical issue due to the number of victims that might be impacted in a short period of time. Any effective rapid emergency response plan for reducing the number of sick people or deaths among those drinking the polluted water requires a reliable forecast of the water contamination zoning map (CZM). The water CZM is a visual representation of the spread of contamination at the time of contamination detection. This study presents a novel methodology based on the rough set theory (RST) for real-time forecasting of the CZM caused by simultaneous multi-point contamination injection in WDS. Our proposed methodology consists of (i) a Monte Carlo simulation model to capture the uncertainties in a multi-point deliberate contamination, (ii) a numerical simulation model for simulating pipe flow, and (iii) a rough set-based technique for real-time CZM for a WDS equipped with a set of monitoring stations. The proposed methodology can be used for any type of random contamination of WDSs as well as emergencies in deliberate contamination of water distribution networks.

A rough set based algorithm for updating the modes in categorical clustering.

The categorical clustering problem has attracted much attention especially in the last decades since many real world applications produce categorical data. The k-mode algorithm, proposed since 1998, and its multiple variants were widely used in this context. However, they suffer from a great limitation related to the update of the modes in each iteration. The mode in the last step of these algorithms is randomly selected although it is possible to identify many candidate ones. In this paper, a rough density mode selection method is proposed to identify the adequate modes among a list of candidate ones in each iteration of the k-modes. The proposed method, called Density Rough k-Modes (DRk-M) was experimented using real world datasets extracted from the UCI Machine Learning Repository, the Global Terrorism Database (GTD) and a set of collected Tweets. The DRk-M was also compared to many states of the art clustering methods and has shown great efficiency.

Accurate Classification of COVID-19 Based on Incomplete Heterogeneous Data using a KNN Variant Algorithm.

Great efforts are now underway to control the coronavirus 2019 disease (COVID-19). Millions of people are medically examined, and their data keep piling up awaiting classification. The data are typically both incomplete and heterogeneous which hampers classical classification algorithms. Some researchers have recently modified the popular KNN algorithm as a solution, where they handle incompleteness by imputation and heterogeneity by converting categorical data into numbers. In this article, we introduce a novel KNN variant (KNNV) algorithm that provides better results as demonstrated by thorough experimental work. We employ rough set theoretic techniques to handle both incompleteness and heterogeneity, as well as to find an ideal value for K. The KNNV algorithm takes an incomplete, heterogeneous dataset, containing medical records of people, and identifies those cases with COVID-19. We use in the process two popular distance metrics, Euclidean and Mahalanobis, in an effort to widen the operational scope. The KNNV algorithm is implemented and tested on a real dataset from the Italian Society of Medical and Interventional Radiology. The experimental results show that it can efficiently and accurately classify COVID-19 cases. It is also compared to three KNN derivatives. The comparison results show that it greatly outperforms all its competitors in terms of four metrics: precision, recall, accuracy, and F-Score. The algorithm given in this article can be easily applied to classify other diseases. Moreover, its methodology can be further extended to do general classification tasks outside the medical field.

Identifying the key factors of subsidiary supervision and management using an innovative hybrid architecture in a big data environment.

In a highly intertwined and connected business environment, globalized layout planning can be an effective way for enterprises to expand their market. Nevertheless, conflicts and contradictions always exist between parent and subsidiary enterprises; if they are in different countries, these conflicts can become especially problematic. Internal control systems for subsidiary supervision and management seem to be particularly important when aiming to align subsidiaries' decisions with parent enterprises' strategic intentions, and such systems undoubtedly involve numerous criteria/dimensions. An effective tool is urgently needed to clarify the relevant issues and discern the cause-and-effect relationships among them in these conflicts. Traditional statistical approaches cannot fully explain these situations due to the complexity and invisibility of the criteria/dimensions; thus, the fuzzy rough set theory (FRST), with its superior data exploration ability and impreciseness tolerance, can be considered to adequately address the complexities. Motivated by efficient integrated systems, aggregating multiple dissimilar systems' outputs and converting them into a consensus result can be useful for realizing outstanding performances. Based on this concept, we insert selected criteria/dimensions via FRST into DEMATEL to identify and analyze the dependency and feedback relations among variables of parent/subsidiary gaps and conflicts. The results present the improvement priorities based on their magnitude of impact, in the following order: organizational control structure, business and financial information system management, major financial management, business strategy management, construction of a management system, and integrated audit management. Managers can consider the potential implications herein when formulating future targeted policies to improve subsidiary supervision and strengthen overall corporate governance.

Three types of logical structure resulting from the trilemma of free will, determinism and locality.

How can one defend free will against determinism? Since quantum mechanics entails non-locality, it enables the co-existence of free will and determinism. Is non-locality in cognition possible, or must quantum mechanics be rejected? Here, we define free will, determinism and locality in terms of a binary relation between objects and representations, and we verify that the three concepts constitute a trilemma. We also show that non-locality in cognition is naturally found in decision making without any assumption of quantum mechanics. Three kinds of relations result from the trilemma. By using a rough set lattice technique, the three kinds of relations can be transformed into three kinds of logical structures. Type I is a naive set theoretical logic or Boolean algebra (i.e., all possible combinations of binary yes-no responses). Type II comprises all possible combinations of various multiple values, such as for the symptoms of schizophrenia. Type III is a non-local disjoint union of multiple contexts. The type III structure can show how non-locality in cognition can lead to the co-existence of free will and determinism. Loss of non-locality could play an essential role in the malfunction of the separation and integration of the self and others.

Fire Control System Operation Status Assessment Based on Information Fusion: Case Study.

In traditional fault diagnosis strategies, massive and disordered data cannot be utilized effectively. Furthermore, just a single parameter is used for fault diagnosis of a weapons fire control system, which might lead to uncertainty in the results. This paper proposes an information fusion method in which rough set theory (RST) is combined with an improved Dempster-Shafer (DS) evidence theory to identify various system operation states. First, the feature information of different faults is extracted from the original data, then this information is used as the evidence of the state for a diagnosis object. By introducing RST, the extracted fault information is reduced in terms of the number of attributes, and the basic probability value of the reduced fault information is obtained. Based on an analysis of conflicts in the existing DS evidence theory, an improved conflict evidence synthesis method is proposed, which combines the improved synthesis rule and the conflict evidence weight allocation methods. Then, an intelligent evaluation model for the fire control system operation state is established, which is based on the improved evidence theory and RST. The case of a power supply module in a fire control computer is analyzed. In this case, the state grade of the power supply module is evaluated by the proposed method, and the conclusion verifies the effectiveness of the proposed method in evaluating the operation state of a fire control system.

[Developing spectrum-toxicity relationship with rough set theory for hepatotoxicity material basis of Polygonum multiflorum].

Idiosyncratic hepatotoxicity of Polygonum multiflorum has attracted a great attention in the world. The most toxic part of idiosyncratic hepatotoxicity was screened by MTT assay and flow cytometry, which was the 50% ethanol elute by macroporous adsorptive resins from alcohol-extraction of P. multiflorum. The fingerprints were collected by HPLC from 50% ethanol elute of crude and processed P. multiflorum from different habitats, then 14 common peaks were determined. Spectrum-toxicity relationship was analyzed by rough set theory(RST). Two main chemical components were predicted for idiosyncratic hepatotoxicity, in which TSG was the greater contributor. Idiosyncratic hepatotoxicity of TSG was tested in vitro, and the results indicated that TSG was the most important constituent contributed to idiosyncratic hepatotoxicity of P. multiflorum. The study showed the discovery of the main chemical components for idiosyncratic hepatotoxicity, and RST was effective for analyzing the spectrum-toxicity relationship, which could be a new method used in the effective/toxic constituents field of traditional Chinese medicine.

Prediction of cancer using customised fuzzy rough machine learning approaches.

This Letter proposes a customised approach for attribute selection applied to the fuzzy rough quick reduct algorithm. The unbalanced data is balanced using synthetic minority oversampling technique. The huge dimensionality of the cancer data is reduced using a correlation-based filter. The dimensionality reduced balanced attribute gene subset is used to compute the final minimal reduct set using a customised fuzzy triangular norm operator on the fuzzy rough quick reduct algorithm. The customised fuzzy triangular norm operator is used with a Lukasiewicz fuzzy implicator to compute the fuzzy approximation. The customised operator selects the least number of informative feature genes from the dimensionality reduced datasets. Classification accuracy using leave-one-out cross validation of 94.85, 76.54, 98.11, and 99.13% is obtained using a customised function for Lukasiewicz triangular norm operator on leukemia, central nervous system, lung, and ovarian datasets, respectively. Performance analysis of the conventional fuzzy rough quick reduct and the proposed method are performed using parameters such as classification accuracy, precision, recall, F-measure, scatter plots, receiver operating characteristic area, McNemar test, chi-squared test, Matthew's correlation coefficient and false discovery rate that are used to prove that the proposed approach performs better than available methods in the literature.

Optimizing the Predictive Ability of Machine Learning Methods for Landslide Susceptibility Mapping Using SMOTE for Lishui City in Zhejiang Province, China.

The main goal of this study was to use the synthetic minority oversampling technique (SMOTE) to expand the quantity of landslide samples for machine learning methods (i.e., support vector machine (SVM), logistic regression (LR), artificial neural network (ANN), and random forest (RF)) to produce high-quality landslide susceptibility maps for Lishui City in Zhejiang Province, China. Landslide-related factors were extracted from topographic maps, geological maps, and satellite images. Twelve factors were selected as independent variables using correlation coefficient analysis and the neighborhood rough set (NRS) method. In total, 288 soil landslides were mapped using field surveys, historical records, and satellite images. The landslides were randomly divided into two datasets: 70% of all landslides were selected as the original training dataset and 30% were used for validation. Then, SMOTE was employed to generate datasets with sizes ranging from two to thirty times that of the training dataset to establish and compare the four machine learning methods for landslide susceptibility mapping. In addition, we used slope units to subdivide the terrain to determine the landslide susceptibility. Finally, the landslide susceptibility maps were validated using statistical indexes and the area under the curve (AUC). The results indicated that the performances of the four machine learning methods showed different levels of improvement as the sample sizes increased. The RF model exhibited a more substantial improvement (AUC improved by 24.12%) than did the ANN (18.94%), SVM (17.77%), and LR (3.00%) models. Furthermore, the ANN model achieved the highest predictive ability (AUC = 0.98), followed by the RF (AUC = 0.96), SVM (AUC = 0.94), and LR (AUC = 0.79) models. This approach significantly improves the performance of machine learning techniques for landslide susceptibility mapping, thereby providing a better tool for reducing the impacts of landslide disasters.