Hybrid Machine Learning Classification Technique for Improve Accuracy of Heart Disease

Project Code :TCMAPY471

Objective

The main Objective of this project is to classify Heart disease using Hybrid classification technique which gives high accuracy.

Abstract

The area of medical science has attracted great attention from researchers. Several causes for human early mortality have been identified by a decent number of investigators. The related literature has confirmed that diseases are caused by different reasons and one such cause is heart based sicknesses. Many researchers proposed idiosyncratic methods to preserve human life and help health care experts to recognize, prevent and manage heart disease. Some of the convenient methodologies facilitate the expert's decision but every successful scheme has its own restrictions.

The proposed approach robustly analyze an act of Decision Tree, Random Forest, XGBoost and Hybrid Model. After analyzing the procedure the intended method smartly builds. Initially, the intention is to select the most appropriate method and analyzing the act of available schemes executed with different features for examining the statistics

NOTE: Without the concern of our team, please don't submit to the college. This Abstract varies based on student requirements.