Analysis of Hybrid Machine Learning Algorithm for Heart Disease Prediction

Abstract

In the current environment of rapid technological and data advancements, the healthcare sector is one of the most exciting fields of research. It\'s challenging to deal with a lot of patient data. Big data analytics makes it simple to process this data. Around the world, numerous treatments exist for various diseases. A novel method that aids in disease prediction in machine learning. In this study, machine learning is used to forecast diseases based on symptoms. The presented data set is used to train machine learning algorithms like Adaboost, XGBoost, CNN, Naive Bayes, Decision Trees, and Random Forests to predict disease. Research reveals the most accurate algorithm. Performance on a specific dataset is what determines an algorithm\'s accuracy. Then We Justify that The Person Having Heart Diseases or Not. With Rapid Advancement in Technology, our model is trained so well that we can predict heart diseases early.