Abstract

Heart disease is still one of the main causes of mortality in the world, and its early diagnosis represents an important part of timely treatment and prevention. The purpose of this work is to create a stable and accurate Machine Learning (ML) model for predicting the risk of heart disease with real clinical patient data. This research work relied on a clinical sample of 333 patient records of Sai Cardiac Hospital, Vijayapura, Karnataka, India (SCHV). The data set included medical parameters that included age, sex, type of chest pain, echo, test outcomes, resting Electrocardiogram (ECG), and Coronary Angiograph (CA) test. Structural pre-processing and visualization tools were employed to determine and derive meaningful predictors of heart disease. Three machine learning classifier models (Support Vector Machine (SVM), K-Nearest Neighbors (KNN), and Random Forest (RF)) were built and evaluated using accuracy, precision, recall, F1 score, specificity, Area Under Curve (AUC), and Matthews Correlation Coefficient (MCC) for performance measurement. Out of the above ML models that were generated, the SVM classifier performed best with an accuracy of 90% and an AUC of 0.95, better than both RF and KNN models. The integrity of the proposed model was verified based on the Receiver Operating Curve (ROC) curve and confusion matrices. Comparison with existing studies showed that the developed SVM model is more reliable for prediction. The results indicate that SVM-based predictive modelling has promising prospects for medical real-time diagnosis and can serve as a candidate decision support system in healthcare practice.

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