Paper Key : IRJ************478
Author: Ramya S ,M.vijayraj
Date Published: 01 Oct 2023
Abstract
According to a recent report by the World Health Organization, heart-related disorders cause 17.9 million deaths per year and are on the rise. The purpose of this study is to analyses different data mining approaches, particularly Naive Thomas Bayes, Random Forest Classification, call trees, and Support Vector Machines are used to predict cardiopathy using a qualified dataset that includes various parameters such as gender, age, type of pain, blood sugar, and pressure level. Finding connections between the dataset's many properties using high-quality data processing techniques is a key component of the research, which also involves treating the attributes appropriately to forecast the likelihood of a cardiopathy. These machine learning approaches forecast illnesses with a high degree of accuracy in a shorter amount of time, which can save the loss of important lives around the globe.