Implementation of Heart Dieses Prediction System using Combination of XGBoost, Logistic Regression and Random Forest
Year: 2023
Type: article
Abstract: In this modern times, Heart Disease prediction is one of the most critical tasks in the world. In recent times, a lot of people have died due to heart disease. Machine learning plays a very important role in training and testing the huge amount of data in the medical field. Heart disease prediction is a crucial task to create and evaluate the prediction process to avoid heart disease and alert the patient before he/she suffers from disease. This research predicts the chances of Heart Disease and says whether the patient has heart disease or not by implementing different machine learning techniques such as Decision Tree, Logistic Regression. Finally, this study shows a result of heart disease and Results are obtained and comparative experiments have shown that the proposed approach can be utilized to give the prediction to the patient.This research predicts the chances of Heart Disease and says whether the patient has heart disease or not by implementing different machine learning techniques such as Decision Tree, Logistic Regression. Finally, this study shows a result of heart disease and Results are obtained and comparative experiments have shown that the proposed approach can be utilized to give the prediction to the patient.
Authors: Prof. Pushpa Chutel, Govind Thawkar, Abhishek Gupta, Aman Thakur, Abhishek Shukla, Anish Karhade, Prashil Meshram
Institution: Raisoni Group of Institutions
Language: English
Subfield: Health Information Management
Field: Health Professions
Domain: Health Sciences
Open Access status: diamond
OpenAlex ID: W4388501336