International Journal of Electrical and Computer Engineering (IJECE) Vol. 13, No. 2, April 2023, pp. 2177~2185 ISSN: 2088-8708, DOI: 10.11591/ijece.v13i2.pp2177-2185  2177 Journal homepage: http://ijece.iaescore.com Involving machine learning techniques in heart disease diagnosis: a performance analysis Ban Salman Shukur 1 , Maad M. Mijwil 2 1 Computer Science Department, Baghdad College of Economic Sciences University, Baghdad, Iraq 2 Computer Techniques Engineering Department, Baghdad College of Economic Sciences University, Baghdad, Iraq Article Info ABSTRACT Article history: Received Apr 30, 2022 Revised Sep 19, 2022 Accepted Oct 14, 2022 Artificial intelligence is a science that is growing at a tremendous speed every day and has become an essential part of many domains, including the medical domain. Therefore, countless artificial intelligence applications can be seen in the medical domain at various levels, which are employed to enhance early diagnosis and prediction and reduce the risks associated with many diseases, including heart diseases. In this article, machine learning techniques (logistic regression, random forest, artificial neural network, support vector machines, and k-nearest neighbors) are utilized to diagnose heart disease from the Cleveland Clinic dataset got from the University of California Irvine machine learning (UCL) repository and Kaggle platform then create a comparison between the performance of these techniques. In addition, some literature related to machine learning and deep learning techniques that aim to provide reasonable solutions in monitoring, detecting, diagnosing, and predicting heart disease and how these technologies assist in making health decisions are reviewed. Ten studies are selected and summarized by the authors published between 2017 and 2022 are illustrated. After executing a series of tests, it is seen that the most profitable performance in diagnosing heart disease is the support vector machines, with a diagnostic accuracy of 96%. This article has concluded that these techniques play a significant and influential role in assisting physicians and health care workers in analyzing heart patients' data, making health decisions, and saving patients' lives. Keywords: Artificial intelligence Cleveland clinic COVID-19 Deep learning Heart diseases Machine learning This is an open access article under the CC BY-SA license. Corresponding Author: Maad M. Mijwil Computer Techniques Engineering Department, Baghdad College of Economic Sciences University Baghdad Province, Yarmouk, Nafaq Al-Shurta, Iraq Email: mr.maad.alnaimiy@baghdadcollege.edu.iq 1. INTRODUCTION Today, computers and computer systems have become a vital part of our lives [1]. Computers are employed in almost every domain [2]. In the past years, computers have been benefited from summarizing large amounts of data over time and making comments about events utilizing these data while performing calculations or transmitting data [3], [4]. Nowadays, computers can make decisions about events and know the relationship between events [5]. Also, computers can also solve issues that cannot be mathematically formulated. The most noteworthy part of computer science is artificial intelligence [6], [7]. This science is distinguished by its ability to suggest computational models of learning based on human biological neural networks [8]. In other words, several models of artificial intelligence have been suggested, which, thanks to advancements in computational technology, have allowed the growth of "intelligent" techniques that facilitate processing more data in less time, speeding up the decision-making process [9], [10]. In addition,