Treffer: A Comparative Analysis and Implementation of Supervised Machine Learning Models for Gui-Based Heart Disease Prediction

Heart failure is a critical global health issue, contributing substantially to morbidity and mortality worldwide. Early and accurate detection is essential for timely intervention and improved patient outcomes. This study presents a comparative analysis and implementation of five supervised machine learning algorithms—Decision Tree (DT), Logistic Regression (LR), Support Vector Machine (SVM), Gaussian Naive Bayes (GNB), and K-Nearest Neighbors (KNN)—for heart disease prediction. An open-access dataset comprising 1,025 patient records with 13 relevant features was used, with a 70:30 training-to-testing split. The performance of each model was evaluated based on accuracy, precision, recall, and F1 score. Results showed that the Decision Tree model outperformed other models, with 94.48% accuracy, 90.79% precision, 96.66% recall, and a 94.56% F1-score. The analysis of hyperparameter tuning max_depth parameter was also analysed to optimise the DT model. Logistic Regression and SVM performed competitively but with lower metrics, while KNN recorded the lowest accuracy, highlighting its limitations for complex datasets. At the end of this study, a Graphical User Interface (GUI) was developed using Python’s Tkinter library. Integrating the optimized Decision Tree model, the GUI allows healthcare practitioners to input 13 clinical parameters and instantly receive heart disease risk predictions. The GUI facilitates the decision-making in patient pre-screening and early intervention treatment. The GUI enhanced diagnostic precision and delivering accessible, cost-effective tools for healthcare settings.