Heart Disease Detection Using AI

Abstract

Over the past few decades, cardiovascular disease has emerged as the primary cause of death worldwide in both industrialized and developing nations. Early detection of heart problems and continued clinical monitoring can reduce death rates. However, because it takes more time and experience, it is not possible to accurately detect heart disorders in all cases and to have a specialist talk with a patient for 24 hours. We demonstrate how machine learning can be used to estimate an individual's risk of developing heart disease. This study presents data processing, which includes converting categorical columns and working with categorical variables. We outline the three primary stages of developing an application: gathering datasets, running logistic regression, and assessing the properties of the dataset. The random forest classifier technique is developed to diagnose cardiac problems more precisely. Data analysis is needed for this application since it is considered noteworthy. The random forest classifier algorithm, which improves the accuracy of research diagnosis, is next covered, along with the experiments and findings.