A proposed tree-based explainable artificial intelligence approach for the prediction of angina pectoris.

Cardiovascular diseases (CVDs) are a serious public health issue that affects and is responsible for numerous fatalities and impairments. Ischemic heart disease (IHD) is one of the most prevalent and deadliest types of CVDs and is responsible for 45% of all CVD-related fatalities. IHD occurs when the blood supply to the heart is reduced due to narrowed or blocked arteries, which causes angina pectoris (AP) chest pain. AP is a common symptom of IHD and can indicate a higher risk of heart attack or sudden cardiac death. Therefore, it is important to diagnose and treat AP promptly and effectively. To forecast AP in women, we constructed a novel artificial intelligence (AI) method employing the tree-based algorithm known as an Explainable Boosting Machine (EBM). EBM is a machine learning (ML) technique that combines the interpretability of linear models with the flexibility and accuracy of gradient boosting. We applied EBM to a dataset of 200 female patients, 100 with AP and 100 without AP, and extracted the most relevant features for AP prediction. We then evaluated the performance of EBM against other AI methods, such as Logistic Regression (LR), Categorical Boosting (CatBoost), eXtreme Gradient Boosting (XGBoost), Adaptive Boosting (AdaBoost), and Light Gradient Boosting Machine (LightGBM). We found that EBM was the most accurate and well-balanced technique for forecasting AP, with accuracy (0.925) and Youden's index (0.960). We also looked at the global and local explanations provided by EBM to better understand how each feature affected the prediction and how each patient was classified. Our research showed that EBM is a useful AI method for predicting AP in women and identifying the risk factors related to it. This can help clinicians to provide personalized and evidence-based care for female patients with AP.

Prediction of Postcoronary Artery Bypass Grafting Atrial Fibrillation: POAFRiskScore Tool.

BACKGROUND: Atrial fibrillation (AF), a condition that might occur after a heart bypass procedure, has caused differing estimates of its occurrence and risk. The current study analyses the possible risk factors of post-coronary artery bypass grafting (post-CABG) AF (postoperative AF [POAF]) and presents a software for preoperative POAF risk prediction. METHODS: This retrospective research was performed on 1,667 patients who underwent CABG surgery using the hospital database. The associations between the variables of the patients and AF risk factors after CABG were examined using multivariable logistic regression (LR) after preprocessing the relevant data. The tool was designed to predict POAF risk using Shiny, an R package, to develop a web-based software. RESULTS: The overall proportion of post-CABG AF was 12.2%. According to the results of univariate tests, in terms of age (p < 0.001), blood urea nitrogen (p = 0.005), platelet (p < 0.001), triglyceride (p = 0.0026), presence of chronic obstructive pulmonary disease (COPD; p = 0.01), and presence of preoperative carotid artery stenosis (PCAS; p < 0.001), there were statistically significant differences between the POAF and non-POAF groups. Multivariable LR analysis disclosed the independent risk factors associated with POAF: PCAS (odds ratio [OR] = 2.360; p = 0.028), COPD (OR = 2.243; p = 0.015), body mass index (OR = 1.090; p = 0.006), age (OR = 1.054, p < 0.001), and platelet (OR = 0.994, p < 0.001). CONCLUSION: The experimental findings from the current research demonstrate that the suggested tool (POAFRiskScore v.1.0) can help clinicians predict POAF risk development in the preoperative period after validated on large sample(s) that can represent the related population(s). Simultaneously, since the updated versions of the proposed tool will be released periodically based on the increases in data dimensions with continuously added new samples and related factors, more robust predictions may be obtained in the subsequent stages of the current study in statistical and clinical terms.

Diagnostic and treatment outcomes of patients with pulmonary tuberculosis in the first year of COVID-19 pandemic.

Background: The COVID-19 pandemic has put a significant strain on human life and health care systems, however, little is known about its impact on tuberculosis (TB) patients. Aims: To assess the impact of COVID-19 pandemic on pulmonary tuberculosis (PTB) diagnosis, treatment and patient outcomes, using the WHO definitions. Methods: A cross-sectional study was conducted in Malatya region, Turkey (population 800 000). Data on regional PTB test numbers, case notification rates and PTB patients' clinical characteristics and treatment outcomes were collected. Data from the first pandemic year (2020) were compared to data from the previous 3 years (2017-2019). The attitudes and experiences of patients were analysed. Results: Despite a non-significant 22% decrease in annual PTB case notifications (P = 0.317), the number of TB tests performed (P = 0.001) and PTB patients evaluated (P = 0.001) decreased significantly during the pandemic year compared with the previous 3 years. The proportion of patients with high (3/4+) sputum acid-fast bacilli grades (P = 0.001), TB relapse (P = 0.022) and treatment failure (P = 0.018) increased significantly. The median 64.5-day treatment delay detected in 2017-2019 increased significantly to 113.5 days in 2020 (P = 0.001), due primarily to patients' reluctance to visit a health care facility. Conclusion: In addition to the problems with case detection, this study shows notable deterioration in several indicators related to the severity, contagiousness and poor outcomes of TB, which had already been suppressed for decades.

Effect of the COVID-19 pandemic on patients with presumed diagnosis of acute appendicitis.

BACKGROUND: Acute appendicitis (AAp) is the most frequent cause of acute abdominal pain, and appendectomy is the most frequent emergency procedure that is performed worldwide. The coronavirus disease 2019 (COVID-19) pandemic has caused delays in managing diseases requiring emergency approaches such as AAp and trauma. AIM: To compare the demographic, clinical, and histopathological outcomes of patients with AAp who underwent appendectomy during pre-COVID-19 and COVID-19 periods. METHODS: The demographic, clinical, biochemical, and histopathological parameters were evaluated and compared in patients who underwent appendectomy with the presumed diagnosis of AAp in the pre-COVID-19 (October 2018-March 2020) and COVID-19 (March 2020-July 2021) periods. RESULTS: Admissions to our tertiary care hospital for AAp increased 44.8% in the COVID-19 period. Pre-COVID-19 (n = 154) and COVID-19 (n = 223) periods were compared for various parameters, and we found that there were statistically significant differences in terms of variables such as procedures performed on the weekdays or weekends [odds ratio (OR): 1.76; P = 0.018], presence of AAp findings on ultrasonography (OR: 15.4; P < 0.001), confirmation of AAp in the histopathologic analysis (OR: 2.6; P = 0.003), determination of perforation in the appendectomy specimen (OR: 2.2; P = 0.004), the diameter of the appendix (P < 0.001), and hospital stay (P = 0.003). There was no statistically significant difference in terms of interval between the initiation of symptoms and admission to the hospital between the pre-COVID-19 (median: 24 h; interquartile range: 34) and COVID-19 (median: 36 h; interquartile range: 60) periods (P = 0.348). The interval between the initiation of symptoms until the hospital admission was significantly longer in patients with perforated AAp regardless of the COVID-19 or pre-COVID-19 status (P < 0.001). CONCLUSION: The present study showed that in the COVID-19 period, the ultrasonographic determination rate of AAp, perforation rate of AAp, and duration of hospital stay increased. On the other hand, negative appendectomy rate decreased. There was no statistically significant delay in hospital admissions that would delay the diagnosis of AAp in the COVID-19 period.

A Retrospective Evaluation of Serological Results of Cystic Echinococcosis Suspected Cases Admitted to Adiyaman Training and Research Hospital Between 2013-2020

Objective: Cystic echinococcosis (CE) is prevalent, especially in animals in Turkey and stands as a significant zoonose. In this study, we aimed to retrospectively evaluate the indirect hemagglutination (IHA) tests results performed on samples of CE suspected patients in microbiology laboratory of our hospital. Methods: One thousand six hundred-seven files of patients admitted to hospital between January 2013 and December 2020 were examined for the presence of anti-E. granulosus immunoglobulin G antibodies. The patient's socio-demographic characteristics and radiological data were obtained from the hospital automatization system. Results: A total of 1.607 file records; 644 (40.1%) males and 963 (59.9%) females, aged between 1-96 years (average 45.26±19.91) were examined. It was found that 244 (15.18%) of the patients were positive, 78 (4.86%) were determined at an intermediary value and 1.285 (79.96%) were negative. According to the IHA method a titer of 1/320 and above were evaluated as positive. Compared to anti-E. granulosus IgG antibody titers 164 radiological data; while 28.6% of 21 patients who are evaluated as negative (1/80) and 46.2% of 78 patients who were evaluated as intermediary titer (1/160) had cystic lesion in the radiological findings. Conclusion: Based on the data, it is suggested that while interpreting the patient's serum antibody titers, patient's clinical and radiological findings should also be taken into account. If possible, it should be used along with another serological method like ELISA to assist CE patient's diagnosis and treatment.

Multi-parameter-based radiological diagnosis of Chiari Malformation using Machine Learning Technology.

BACKGROUND: The known primary radiological diagnosis of Chiari Malformation-I (CM-I) is based on the degree of tonsillar herniation (TH) below the Foramen Magnum (FM). However, recent data also shows the association of such malformation with smaller posterior cranial fossa (PCF) volume and the anatomical issues regarding the Odontoid. This study presents the achieved result regarding some detected potential radiological findings that may aid CM-I diagnosis using several machine learning (ML) algorithms. MATERIALS AND METHODS: Midsagittal T1-weighted MR images were collected in 241 adult patients diagnosed with CM, eleven morphometric measures of the posterior cerebral fossa were performed. Patients whose imaging was performed in the same centre and on the same device were included in the study. By matching age and gender, radiological exams of 100 clinically/radiologically proven symptomatic CM-I cases and 100 healthy controls were assessed. Eleven morphometric measures of the posterior cerebral fossa were examined using 5 designed ML algorithms. RESULTS: The mean age of patients was 29.92 ± 15.03 years. The primary presenting symptoms were headaches (62%). Syringomyelia and retrocurved-odontoid were detected in 34% and 8% of patients, respectively. All of the morphometric measures were significantly different between the groups, except for the distance from the dens axis to the posterior margin of FM. The Radom Forest model is found to have the best 1.0 (14 of 14) ratio of accuracy in regard to 14 different combinations of morphometric features. CONCLUSION: Our study indicates the potential usefulness of ML-guided PCF measurements, other than TH, that may be used to predict and diagnose CM-I accurately. Combining two or three preferable osseous structure-based measurements may increase the accuracy of radiological diagnosis of CM-I.

Prediction of death status on the course of treatment in SARS-COV-2 patients with deep learning and machine learning methods.

BACKGROUND AND OBJECTIVE: The new type of Coronavirus (2019-nCov) epidemic spread rapidly, causing more than 250 thousand deaths worldwide. The virus, which first appeared as a sign of pneumonia, was later called the SARS-COV-2 with Severe Acute Respiratory Syndrome by the World Health Organization. The SARS-COV-2 virus is triggered by binding to the Angiotensin-Converting Enzyme 2 (ACE 2) inhibitor, which is vital in cardiovascular diseases and the immune system, especially in conditions such as cerebrovascular, hypertension, and diabetes. This study aims to evaluate the prediction performance of death status based on the demographic/clinical factors (including COVID-19 severity) by data mining methods. METHODS: The dataset consists of 1603 SARS-COV-2 patients and 13 variables obtained from an open-source web address. The current dataset contains age, gender, chronic disease (hypertension, diabetes, renal, cardiovascular, etc.), some enzymes (ACE, angiotensin II receptor blockers), and COVID-19 severity, which are used to predict death status using deep learning and machine learning approaches (random forest, k-nearest neighbor, extreme gradient boosting [XGBoost]). A grid search algorithm tunes hyperparameters of the models, and predictions are assessed through performance metrics. Steps of knowledge discovery in databases are applied to obtain the relevant information. RESULTS: The accuracy rate of deep learning (97.15%) was more successful than the accuracy rate based on classical machine learning (92.15% for RF and 93.4% for k-NN), but the ensemble classifier XGBoost method gave the highest accuracy (99.7%). While COVID-19 severity and age calculated from XGBoost were the two most important factors associated with death status, the most determining variables for death status estimated from deep learning were COVID-19 severity and hypertension. CONCLUSIONS: The proposed model (XGBoost) achieved the best prediction of death status based on the factors as compared to the other algorithms. The results of this study can guide patients with certain variables to take early measures and access preventive health care services before they become infected with the virus.

The role of physical activity on mental health and quality of life during COVID-19 outbreak: A cross-sectional study.

INTRODUCTION: The COVID-19 pandemic has placed restrictions on people's physical activities. The aim of this study was to evaluate the physical activity levels of individuals and assess the effects of physical activity on quality of life, depression and anxiety levels during the COVID-19 outbreak. METHODS: This cross-sectional study were included 2301 participants aged 20-75 years. The data were collected through the Google Forms web survey platform by the virtual snowball sampling method. In the multivariate analysis, the independent predictors were analyzed using possible factors identified in previous analyses by multinomial logistic regression analysis. Hosmer-Lemeshow and Omnibus tests were used to evaluate the logistic regression model and coefficients. RESULTS: The mean weekly energy consumption of the participants was 875±1588 MET-min, and only 6.9% were physically active enough to maintain their health. There was a weak positive relationship between physical activity levels and quality of life, while there was a weak negative relationship between physical activity levels, depression and anxiety (p<0.05). In the multinomial logistic regression model established for comparison of physically active and inactive participants, general health status and physical health status variables were statistically significant (p<0.05). However, relationships between psychological status, social relationships and environment scores, Beck Depression and Beck Anxiety Inventory scores were not statistically significant (p>0.05). CONCLUSIONS: Results showed that physical activity programs should be included in guidelines as an integrative approach to pandemic management. During COVID-19 outbreak, community-based rehabilitation programs are needed, and these programs should be carried out in cooperation with community stakeholders.