Heart Disease Prediction using Machine Learning Algorithms

Heart disease is one of the main causes of death globally, and successful treatment depends on precise diagnosis and prognosis. However, predicting heart disease is challenging and requires expertise due to the potential consequences of errors. Large datasets from data repositories are available for real-world applications. The proposed system utilizes a dataset of patient medical records, which includes several features such as age, sex, blood pressure, cholesterol levels, and smoking status, among others. These characteristics are employed in the training and testing of various machine learning algorithms, such as support vector machines, random forests, and decision trees, to forecast the probability that a patient would have cardiac disease. we have used K-Nearest Neighbour, Random Forest, Logistic Regression, Support Vector Machine (SVM), and XG-boost. Our goal is to find hidden patterns and choose an appropriate machine learning method for heart disease prediction that is accurate and computationally efficient.