Cardiovascular diseases (CVDs) are the leading cause of death worldwide, representing a significant public health challenge. Myocardial Infarction (MI), a severe manifestation of CVDs, contributes substantially to these fatalities. Machine learning holds great promise for predicting MI. This study explores the potential of Gradient Boosting (GB) techniques for this purpose, explicitly focusing on CatBoost, LightGBM, XGBoost, and XGBoost Random Forest. The study leverages GB's embedded feature selection, missing-value handling, and hyperparameter tuning capabilities. Performance was evaluated using multiple metrics: Area Under the Curve (AUC), classification accuracy, F1 score, precision, recall, and Matthews Correlation Coefficient (MCC). A probabilistic comparison matrix was used to assess the relative performance of the GB models. The results demonstrate the superiority of CatBoost, achieving a classification accuracy of 94.9%, an AUC of 0.992, a recall of 94.9%, and an MCC of 0.82. The probabilistic comparison further confirms CatBoost's superior performance. These findings contribute to MI prediction, highlighting the predictive potential of the CatBoost algorithm and ultimately aiding the fight against MI to achieve better patient outcomes.

The Semantic Web enhances data interoperability and enables intelligent information retrieval through structured data representation. However, challenges remain in achieving high precision in semantic search. This paper uses the Continuous Bag of Words (CBOW) model to enhance semantic search precision. By generating rich word embeddings, CBOW enables a better understanding of contextual relationships among terms within semantic queries. Our approach has been evaluated using the websites intended to be used as a sample for testing the efficiency of semantic information retrieval, demonstrating significant improvements in search precision compared to traditional methods. The findings indicate that integrating CBOW into semantic search frameworks can lead to more relevant and accurate search results, paving the way for future advancements in Semantic Web technologies.

Under-five mortality remains a significant public health challenge in the Central African Republic (CAR), yet research on the region-specific determinants of this issue is limited. This study explores the use of machine learning (ML) models to predict under-five mortality in CAR using data from the 2018–2019 Multiple Indicator Cluster Survey (MICS). Advanced ML models, including CatBoost (CATB), Light Gradient Boosting (LGB), Extreme Gradient Boosting (XGBoost), and Extreme Gradient Boosting Random Forest (XGBRF), were compared with traditional logistic regression, with model performance evaluated using accuracy metrics such as the Area Under the Curve (AUC), F1 score, Precision, Recall, and Matthews Correlation Coefficient (MCC). CATB outperformed all models, achieving an AUC of 0.973, accuracy of 0.946, F1 score of 0.951, and MCC of 0.897, demonstrating its robust predictive power. The study identified key factors influencing under-five mortality in CAR, such as birth type, maternal education, birth order, birth interval, and birth weight. It suggests that using advanced ML models, particularly CATBoost, can improve identifying region-specific risk factors and inform targeted interventions to reduce child mortality in CAR.

This study presents an exploratory data analysis and visualisation of mental health data in South Sudan, a total of 72,414 reported cases collected between 2020 and 2022 from the National Ministry of Health via the DHIS 2. The Demographic Health Information System 2 (DHIS 2) is a data collection tool that collects data from the health facilities across the country. The mental health statistical analysis covers data for acute stress, depression, psychosis, grief, post-traumatic stress disorder, substance and drug abuse, suicide attempts, other mental health conditions and other significant mental health complaints. The study identifies trends in mental health data needed by the health providers. The detailed data analysis will facilitate data-driven interventions, planning, and monitoring of the mental health programme. The diagnosis of mental health cases was classified using the Diagnostic and Statistical Manual of Mental Disorders (DSM). The exploratory data analysis (EDA) conducted using R statistical software (R Markdown) provides good visualisations and tabulations of the mental health data. Substance & drugs abuse, post-traumatic stress disorders, suicide attempts, grief, depression and acute stress are the common mental problems in South Sudan. The data show that the acute mental health cases are increasing at a rate of 41 per cent annually. The findings are useful for re-programming mental health interventions and increasing public awareness about mental health.

Background: Premarital screening (PMS) is a nationwide program that helps high-risk individuals make decisions to avoid genetic and sexually transmitted diseases from spreading to their spouse or future offspring. This study examined the knowledge and attitudes towards inherited hemoglobinopathies in PMS among the people of Northern Border Region in Saudi Arabia and their relationship to various sociodemographic factors. Methods: A cross-sectional study was undertaken in the Northern region of Saudi Arabia from January to March 2024. Data were gathered via questionnaire from 478 Saudi participants aged 18 years and older. The chi-square test was employed to determine the association between categorical variables. Results: All participants in the study were familiar with the PMS program. A significant portion of participants, 79.3%, acknowledged that consanguinity can increase the risk of hereditary blood disorders, while 69.9% believed that if both parents are carriers of the same genetic blood disease, their child may inherit it. Higher education, female gender, and age group (30–40) were found to be the main predictors of knowledge regarding PMS. Most of the participants (98.5%) had a positive attitude regarding the necessity of PMS as a prerequisite for marriage completion. About 82.8% indicated they would not continue with the marriage if the PMS results were incompatible. Conclusions: The study indicates a growing awareness and positive attitude towards premarital screening among the general population, with an increasing number of individuals opting for it. The findings suggest that PMS programs contribute to informed decision making, as evidenced by the rise in participants choosing to forgo marriage due to partner incompatibility. The study recommends the enhancement of health education campaigns by considering demographic factors such as age, education, and marital status. Additionally, it advocates for expanding the scope of PMS to include a wider range of health and genetic disorders to improve its overall efficacy.