Heart Disease Prediction and Diagnostic Analysis

A heart disease is a significant health concern worldwide and is responsible for a considerable number of deaths every year. Early detection of heart disease can significantly reduce the risk of morbidity and mortality associated with it. This has led to the development of various machine learning techniques for predicting and analyzing heart disease. This study investigates the pertinence of machine learning techniques to predict long term outcomes in heart disease patient. The study aims to evaluate the performance of different machine learning algorithms such as Decision tree, KNN, and SVM for predicting the occurrence of heart disease in patients. The data used in this project is collected from Kaggle Platform. The performance of each algorithm is evaluated using metrics which is accuracy and the results of this study will provide valuable insights into the effectiveness of different Machine learning algorithms for heart disease prediction and analysis, which can be used for early detection and management of heart disease. Varieties of data points that could lead to heart disease are examined. The diagnostic analysis additionally demonstrates how some factors, like Age, Sex, Cholestrol, CP, Restbps etc. used in data-sheet are likely to cause heart disease. Tableau has been used for Data Visualization. Django has been used as web framework. User have to enter required input value and then prediction analysis will result whether patient having heart disease or not