Machine Learning Model for Heart Disease Detection: A Comparative Analysis of SVM vs KNN

Out of many critical medical issues, heart disease is one that primarily impacts the cardiovascular system, including the heart and its associated organs. Individuals who smoke, suffer from high blood pressure, have elevated cholesterol levels, maintain poor dietary habits, lack physical activity, or are overweight or obese face a greater risk of developing heart disease. The most frequently diagnosed cardiovascular condition is coronary artery disease (CAD), although other forms of heart disease include congestive heart failure, arrhythmias, and congenital heart defects. This condition is often referred to as cardiovascular disease. Timely and precise diagnosis is essential for saving lives and preventing further complications. Recent advancements in machine learning diagnostic techniques have demonstrated enhanced reliability and effectiveness in detecting heart disease. In this work, we have implemented a SVM algorithm followed by a KNN algorithm to accurately identify the early stages of heart disease within a specific dataset. The algorithms consider various factors, including age, gender, resting blood pressure, cholesterol levels, blood sugar levels, and ECG results when making predictions. The experimental findings reveal that the SVM model outperforms the others, achieving an impressive accuracy rate of 89% on the UCI dataset.