Estimation of disease risk by artificial learning

The aim of the study is to find possible heart disease in the future of healthy people and to classify them into grades such as low risk, medium risk and very risky. So, first of all, the data of people with heart disease in the past will be needed. These acquired data include age, gender, diabetes, kidney failure, and operations such as surgery. These mixed data will first be tried artificial learning methods to distinguish between classes. Artificial neural networks, decision trees and random forest classification algorithms, which are one of the artificial learning methods, will be used. These algorithms have been chosen because many of the data are categorical data. After classifying the data, some test results from healthy people and artificial learning methods based on personality traits will be tested to find out if these people are sick and sick if they are sick.