Paper Title
Heart Disease Identification using Machine Learning Algorithms – A Comparative Approach

Abstract
In living things, the heart plays a crucial role. Greater accuracy, precision, and completeness are required for heart disease diagnosis and prediction. since minor mistakes can cause tiredness issues or even death. Heart-related deaths are exceedingly common, and the number is rising dramatically every day. To solve this problem, a system that forecasts disease awareness is necessary. Artificial intelligence (AI) has a branch called machine learning that provides excellent assistance in forecasting all kinds of events using data from real-world occurrences. Using the UCI repository dataset for training and testing, this post determines the precision of a machine learning algorithm for predicting heart disease. K-nearest neighbours, decision trees, linear regression, and support vector machines are some of these methods (SVM). Anaconda (Jupiter) Notebook is the ideal software for putting Python programming into practise. It includes many types of libraries and header files that let you produce exact work. Keywords - Supervised; Unsupervised; Reinforced; Linear Regression; Decision Tree; Python Programming; Jupiter Notebook; Confusion Matrix;