A Detailed Analysis of Detecting Heart Diseases Using Artificial Intelligence Methods

Abstract

Hearts are crucial for maintaining a healthy lifestyle and are ranked high among the organs that need special care. Globally, heart disease is one of the leading causes of death, posing a considerable public health challenge in low-income countries in particular. Early diagnosis and the identification of risk factors are critical when dealing with these diseases, as early symptoms are often not evident. Heart disease can be caused by several factors, including smoking, poor diet, stress, a lack of physical activity, and excessive alcohol consumption. During the diagnosis process, doctors may encounter various challenges, including vague symptoms, misleading test results, and other medical complications. It is currently possible to diagnose heart disease more accurately and effectively using machine learning algorithms. The present study examines seven different machine learning algorithms on a dataset consisting of 4,238 records and 16 different patient characteristics. Among the classification models, Naive Bayes, Decision Trees, Random Forests, Support Vector Machines (SVM), Artificial Neural Networks (ANNs), K Nearest Neighbors, and Logistic Regressions yielded 78.9%, 79.9%, 83.9%, 70.9%, 83.7%, 83.4%, and 85.5%, respectively.