Research Article Identification of Cardiac Patients Based on the Medical Conditions Using Machine Learning Models Krishna Kumar , 1 Narendra Kumar, 2 Aman Kumar , 3,4 Mazin Abed Mohammed , 5 Alaa S. Al-Waisy, 6 Mustafa Musa Jaber, 7,8 Neeraj Kumar Pandey , 2 Rachna Shah, 9 Gaurav Saini, 10 Fatma Eid , 11 and Mohammed Nasser Al-Andoli 12 1 Department of Hydro and Renewable Energy, Indian Institute of Technology, Roorkee 247667, India 2 School of Computing, DIT University, Dehradun 248009, Uttarakhand, India 3 AcSIR-Academy of Scientific and Innovative Research, Ghaziabad 201002, India 4 Structural Engineering Department, CSIR-Central Building Research Institute, Roorkee 247667, India 5 College of Computer Science and Information Technology, University of Anbar, Anbar 31001, Iraq 6 Computer Technologies Engineering Department, Information Technology Collage, Imam Ja’afar Al-Sadiq University, Baghdad, Iraq 7 Department of Computer Science, Dijlah University College, Baghdad, Iraq 8 Department of Medical Instruments Engineering Techniques, Al-Farahidi University, Baghdad 10021, Iraq 9 Department of CSE, Indian Institute of Information Technology, Guwahati 781015, India 10 Indian Institute of Engineering Science and Technology (IIEST), Shibpur, West Bengal 711103, India 11 Technology Management, College of Business, Stony Brook University, Stony Brook, NY, USA 12 Computer Science & Information Systems Department, Faculty of Science, Sa’adah University, Sa’adah, Yemen Correspondence should be addressed to Mohammed Nasser Al-Andoli; mnalandoli@saada-uni.edu.ye Received 28 April 2022; Accepted 25 June 2022; Published 20 July 2022 Academic Editor: Dalin Zhang Copyright © 2022 Krishna Kumar et al. is is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Chronic diseases are the most severe health concern today, and heart disease is one of them. Coronary artery disease (CAD) affects blood flow to the heart, and it is the most common type of heart disease which causes a heart attack. High blood pressure, high cholesterol, and smoking significantly increase the risk of heart disease. To estimate the risk of heart disease is a complex process because it depends on various input parameters. e linear and analytical models failed due to their assumptions and limited dataset. e existing studies have used medical data for classification purposes, which help to identify the exact condition of the patient, but no one has developed any correlation equation which can be directly used to identify the patients. In this paper, mathematical models have been developed using the medical database of patients suffering from heart disease. Curve fitting and artificial neural network (ANN) have been applied to model the condition of patients to find out whether the patient is suffering from heart disease or not. e developed curve fitting model can identify the cardiac patient with accuracy, having a coefficient of determination (R 2 -value) of 0.6337 and mean absolute error (MAE) of 0.293 at a root mean square error (RMSE) of 0.3688, and the ANN-based model can identify the cardiac patient with accuracy having a coefficient of determination (R 2 -value) of 0.8491 and MAE of 0.20 at RMSE of 0.267, it has been found that ANN provides superior mathematical modeling than curve fitting method in identifying the heart disease patients. Medical professionals can utilize this model to identify heart patients without any an- giography or computed tomography angiography test. Hindawi Computational Intelligence and Neuroscience Volume 2022, Article ID 5882144, 15 pages https://doi.org/10.1155/2022/5882144