Early Detection and Prediction of Heart Disease with Machine Learning Techniques

Abstract

Heart disease is still a major health problem around the world, which shows how important it is to have accurate and quick ways to find and identify diseases. The goal of this study is to use advanced machine learning methods to make it easier to find and predict heart problems early on. Our suggested method uses a special mix of techniques for preparing data, feature selection, and ensemble learning to make forecasts more accurate. The sample that was used in this study includes a lot of different information about the patients, like their age, gender, blood pressure, and cholesterol levels. As part of preparing the data, we fill in any blanks with values, make sure that features are normal, and store category factors. We also use feature selection methods, such as iterative feature removal and association analysis, to find the most important features for forecast. When it comes to building models, we carefully check how well cutting-edge machine learning methods as Random Forest, Gradient Boosting, and Deep Neural Networks work. It also look into how ensemble learning methods, like AdaBoost, can be used to make our estimates more accurate. It use many types of performance measures, such as accuracy, precision, recall, and F1-score, to judge the suggested method. Our results make it clear that the suggested way is better than the usual models and can accurately predict heart disease better. This study shows a new way to quickly find and predict heart disease by using advanced machine learning methods. Proposed method has been shown to greatly improve the accuracy of predictions and provide useful support to healthcare workers. This lets them quickly make choices based on good information about how to handle heart problems

Topics & Concepts