Research Article Machine Learning-Based Model to Predict Heart Disease in Early Stage Employing Different Feature Selection Techniques Niloy Biswas , 1 Md Mamun Ali , 1 Md Abdur Rahaman, 1 Minhajul Islam , 1 Md. Rajib Mia , 1 Sami Azam , 2 Kawsar Ahmed , 3,4 Francis M. Bui , 4 Fahad Ahmed Al-Zahrani , 5 and Mohammad Ali Moni 6 1 Department of Software Engineering (SWE), Daffodil International University (DIU), Sukrabad, Dhaka 1207, Bangladesh 2 College of Engineering, IT, and Environment, Charles Darwin University, Casuarina, NT 0909, Australia 3 Group of Biophotomatiχ, Department of Information and Communication Technology, Mawlana Bhashani Science and Technology University, Santosh, Tangail 1902, Bangladesh 4 Department of Electrical and Computer Engineering, University of Saskatchewan, 57 Campus Drive, Saskatoon, SK, S7N 5A9, Canada 5 Department of Computer Engineering, Umm Al-Qura University, Mecca 24381, Saudi Arabia 6 Artificial Intelligence & Digital Health, School of Health and Rehabilitation Sciences, Faculty of Health and Behavioural Sciences, The University of Queensland, St. Lucia, QLD 4072, Australia Correspondence should be addressed to Kawsar Ahmed; kawsar.ict@mbstu.ac.bd and Mohammad Ali Moni; m.moni@uq.edu.au Received 21 October 2022; Revised 8 December 2022; Accepted 1 April 2023; Published 2 May 2023 Academic Editor: Alejandro L. Borja Copyright © 2023 Niloy Biswas et al. This 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. Almost 17.9 million people are losing their lives due to cardiovascular disease, which is 32% of total death throughout the world. It is a global concern nowadays. However, it is a matter of joy that the mortality rate due to heart disease can be reduced by early treatment, for which early-stage detection is a crucial issue. This study is aimed at building a potential machine learning model to predict heart disease in early stage employing several feature selection techniques to identify significant features. Three different approaches were applied for feature selection such as chi-square, ANOVA, and mutual information, and the selected feature subsets were denoted as SF1, SF2, and SF3, respectively. Then, six different machine learning models such as logistic regression (C1), support vector machine (C2), K-nearest neighbor (C3), random forest (C4), Naive Bayes (C5), and decision tree (C6) were applied to find the most optimistic model along with the best-fit feature subset. Finally, we found that random forest provided the most optimistic performance for SF3 feature subsets with 94.51% accuracy, 94.87% sensitivity, 94.23% specificity, 94.95 area under ROC curve (AURC), and 0.31 log loss. The performance of the applied model along with selected features indicates that the proposed model is highly potential for clinical use to predict heart disease in the early stages with low cost and less time. 1. Introduction Nowadays, machine learning algorithms are vastly used all over the world. In the healthcare industry, machine learning is widely used for predicting disease at an early stage. It saves a lot of people’s lives worldwide by predicting their disease at an early stage. Even then, every year, thousands of people are affected and died from heart disease. If machines can predict the early stage of the disease, then, this prediction should reduce the death risk of heart disease. The heart is a significant limb of the human body, and heart disease is the major reason for death in the present world. When it is unable to perform properly, various limbs are obstructed, and then, the brain and several limbs do not work, and a person will die within a few seconds. It is one of the foremost diseases that most com- monly affects middle or old-aged people and creates severe complications in the human body [1]. It is difficult to diagnose heart disease because of the number of risk factors. The main Hindawi BioMed Research International Volume 2023, Article ID 6864343, 15 pages https://doi.org/10.1155/2023/6864343