Several machine learning classification algorithms were evaluated using a dataset focused on heart failure. Results obtained from logistic regression, random forest, decision tree, K-nearest neighbors, and multilayer perceptron (MLP) were compared to obtain the best model. The random forest method obtained specificity = 0.93, AUC = 0.97, and Matthews correlation coefficient (MCC) = 0.83. The accuracy was high; therefore, it was considered the best model. On the other hand, K-nearest neighbors and MLP (multi-layer perceptron) showed lower accuracy rates. These results confirm the effectiveness of the random forest method in identifying heart failure cases. This study underlines that the number of features, feature selection and quality, model type, and hyperparameter fit are also critical in these studies, as well as the importance of using machine learning techniques.

Diabetes is a chronic metabolic disorder characterized by persistent hyperglycemia. The globally rising prevalence of diabetes has made early and accurate diagnosis imperative to avoid long-term sequelae and decrease the cost burden on health facilities. Machine Learning (ML) has emerged as a highly effective tool of clinical science because of its capability of discovering complex patient data patterns and diagnostic performance optimization. This paper is a comparative performance evaluation of five ML models—Decision Tree (DT), Random Forest (RF), Logistic Regression (LR), K-Nearest Neighbors (KNN), and Multilayer Perceptron (MLP)—utilized for diabetes prediction. Data were processed using the “Healthcare Diabetes Dataset,” made up of eight commonly used clinical parameters. For making the models trustworthy, a strong data preprocessing pipeline was utilized, made up of outlier detection using the Interquartile Range (IQR), normalization of data, and class balancing using the Synthetic Minority Over-sampling Technique (SMOTE). Results reveal that the RF and DT models achieved the highest performance based on accuracy rates of 98.15% and 97.51%, respectively, though more moderate outcomes were recorded by LR and MLP. They reveal the remarkable potential of ML models, particularly ensemble-based ML models such as RF, at supporting early diagnosis of diabetes. When implemented complementarily to clinical decision-making processes, these models can serve a cost-effective and effective replacement of conventional diagnostic methods.