EARLY HEART ATTACK DETECTION

Early detection of heart attack, plays a crucial role in preventing serious complications and improving patient outcomes. This paper presents study on the use machine learning algorithms for early attack detection. The dataset used this includes information such as age, sex, chest pain type, resting blood pressure, cholesterol level, other relevant attributes. Various algorithms, including Support Vector Machine (SVM), Naive Bayes, K-Nearest Neighbors (KNN), Decision Tree, were applied to predict likelihood disease. SVM algorithm showed highest accuracy score was selected further analysis. performance model evaluated using confusion matrix, classification accuracy, error. results demonstrate potential accurately identifying patients with disease based medical factors. findings contribute development an effective system.