Efficient Automated Model for Predicting and Detecting the Heart Disease Through Machine Learning

Abstract

Using machine learning, numerous researchers have created a variety of disease prediction models in the contemporary environment. By using prediction models early on, the death rate could be reduced. This work presents the design of an automated database diagnosis model using machine learning. Here, for study purposes, we chose one of the most common diseases, heart illness. This study presents the preparation of a database of patients with heart disease, followed by analysis in the produced database using a pre-trained machine learning model that was trained on the disease detection result that is displayed on the screen. To do the computation for prediction, a single logistic regression is utilized. Determining the risk of heart disease can be aided by early identification and prediction of the condition. Additionally, a comparison study might aid medical professionals in prescribing medication on time.