Research Article Prediction of Cardiovascular Disease on Self-Augmented Datasets of Heart Patients Using Multiple Machine Learning Models Sumaira Ahmed , 1 Salahuddin Shaikh, 1 Farwa Ikram, 2 Muhammad Fayaz , 3 Hathal Salamah Alwageed , 4 Faheem Khan, 5 and Fawwad Hassan Jaskani 6 1 Centre of Computing Research, Department of Computer Science and Software Engineering, Jinnah University for Women, Karachi 74600, Pakistan 2 Department of Computer Engineering, University of Lahore, Pakistan 3 Department of Computer Science, University of Central Asia, Naryn, Kyrgyzstan 4 College of Computer and Information Science, Jouf University, Saudi Arabia 5 Gachon University, Department of Computer Engineering, Republic of Korea 6 Department of Computer Systems Engineering, Islamia University of Bahawalpur, Bahawalpur 63100, Pakistan Correspondence should be addressed to Muhammad Fayaz; muhammad.fayaz@ucentralasia.org Received 24 June 2022; Revised 14 October 2022; Accepted 27 October 2022; Published 23 December 2022 Academic Editor: Rajesh Kaluri Copyright © 2022 Sumaira Ahmed 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. About 26 million people worldwide experience its effects each year. Both cardiologists and surgeons have a tough time determining when heart failure will occur. Classification and prediction models applied to medical data allow for enhanced insight. Improved heart failure projection is a major goal of the research team using the heart disease dataset. The probability of heart failure is predicted using data mined from a medical database and processed by machine learning methods. It has been shown, through the use of this study and a comparative analysis, that heart disease may be predicted with high precision. In this study, researchers developed a machine learning model to improve the accuracy with which diseases like heart failure (HF) may be predicted. To rank the accuracy of linear models, we find that logistic regression (82.76 percent), SVM (67.24 percent), KNN (60.34 percent), GNB (79.31 percent), and MNB (72.41) perform best. These models are all examples of ensemble learning, with the most accurate being ET (70.31%), RF (87.03%), and GBC (86.21%). DT (ensemble learning models) achieves the highest degree of precision. CatBoost outperforms LGBM, HGBC, and XGB, all of which achieve 84.48% accuracy or better, while XGB achieves 84.48% accuracy using a gradient-based gradient method (GBG). LGBM has the highest accuracy rate (86.21 percent) (hypertuned ensemble learning models). A statistical analysis of all available algorithms found that CatBoost, random forests, and gradient boosting provided the most reliable results for predicting future heart attacks. 1. Introduction Patients often undergo a battery of tests, putting them under unnecessary mental, emotional, and financial strain. Tobacco use, excessive body fat, and cardiovascular disease have all been linked in studies [1]. Pain in the arms and chest is the most common indicator. Cardiac surgeons can benefit from a thorough examination of such a dataset for both diagnostic and operational purposes [2]. It has been attempted in the past [2] to enhance the HF diagnostic pro- cess through the use of learning machines and heart disease categories. This project aims at exploring different machine learning techniques and making better use of healthcare data. It is anticipated that classifier efficiency would rise. Heart failure (HF) and other health risks are affected by an individual’s unique set of circumstances. Standard HF risk prediction models consider each variable as a covariate, but this approach ignores important characteristics like cardiac Hindawi Journal of Sensors Volume 2022, Article ID 3730303, 21 pages https://doi.org/10.1155/2022/3730303