An Advanced Heart Disease Prediction Model with Gradient Boosting: A Data-Driven Approach

Heart disease has become the world wide health criteria. Machine learning plays a key role in detecting and providing more basis for prediction and making decisions using data. This paper includes machine learning techniques for predicting the problems faced by the patients.By exploiting algorithms such as Logistic Regression, Random forest, Decision Tree, Support Vector Machine [SVM], Gradient Boosting, Ada Boost, XGB classifier, we develop a model for classifying patients into high and low categories.By analyzing different classification algorithms and we observed that the accuracy of Gradient Boosting Classifier model-99% is found as the best model among all. Gradient Boosting classifier is based on the concept of boosting, where models are trained in order, and every new model is introduced to correct the errors made by previous model. Gradient Boosting is renowned for its ability to handle complex datasets, capture feature interactions, and deliver high accuracy, making it widely used in applications like healthcare, finance, marketing, and natural language processing. Heart disease cannot be cured easily i.e, which leads to death in the early stage.In the world highest death case is due to heart disease when compare to other diseases. The paper concludes by offering recommendations for future research of heart disease prediction by using ML algorithms.