EARLY HEART ATTACK DETECTION

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