Research Article Effectiveness of Artificial Intelligence Models for Cardiovascular Disease Prediction: Network Meta-Analysis Yahia Baashar , 1 Gamal Alkawsi , 2 Hitham Alhussian , 3 Luiz Fernando Capretz , 4 Ayed Alwadain , 5 Ammar Ahmed Alkahtani , 6 and Malek Almomani 7 1 College of Graduate Studies, Universiti Tenaga Nasional (UNITEN), Selangor, Malaysia 2 Faculty of Computer Science and Information Systems, amar University, amar, Yemen 3 Department of Computer and Information Sciences, Universiti Teknologi PETRONAS, Seri Iskandar, Malaysia 4 Department of Electrical and Computer Engineering, Western University, London, ON, Canada 5 Computer Science Department, Community College, King Saud University, Riyadh, Saudi Arabia 6 Institute of Sustainable Energy, Universiti Tenaga Nasional (UNITEN), Selangor, Malaysia 7 Department of Software Engineering, e World Islamic Sciences and Education University, Amman, Jordan Correspondence should be addressed to Gamal Alkawsi; gamalalkawsi@tu.edu.ye Received 28 November 2021; Accepted 18 January 2022; Published 24 February 2022 Academic Editor: Ahmed Mostafa Khalil Copyright © 2022 Yahia Baashar 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. Heart failure is the most common cause of death in both males and females around the world. Cardiovascular diseases (CVDs), in particular, are the main cause of death worldwide, accounting for 30% of all fatalities in the United States and 45% in Europe. Artificial intelligence (AI) approaches such as machine learning (ML) and deep learning (DL) models are playing an important role in the advancement of heart failure therapy. e main objective of this study was to perform a network meta-analysis of patients with heart failure, stroke, hypertension, and diabetes by comparing the ML and DL models. A comprehensive search of five electronic databases was performed using ScienceDirect, EMBASE, PubMed, Web of Science, and IEEE Xplore. e search strategy was performed according to the Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA) statement. e methodological quality of studies was assessed by following the Quality Assessment of Diagnostic Accuracy Studies 2 (QUADAS-2) guidelines. e random-effects network meta-analysis forest plot with categorical data was used, as were subgroups testing for all four types of treatments and calculating odds ratio (OR) with a 95% confidence interval (CI). Pooled network forest, funnel plots, and the league table, which show the best algorithms for each outcome, were analyzed. Seventeen studies, with a total of 285,213 patients with CVDs, were included in the network meta-analysis. e statistical evidence indicated that the DL algorithms performed well in the prediction of heart failure with AUC of 0.843 and CI [0.840–0.845], while in the ML algorithm, the gradient boosting machine (GBM) achieved an average accuracy of 91.10% in predicting heart failure. An artificial neural network (ANN) performed well in the prediction of diabetes with an OR and CI of 0.0905 [0.0489; 0.1673]. Support vector machine (SVM) performed better for the prediction of stroke with OR and CI of 25.0801 [11.4824; 54.7803]. Random forest (RF) results performed well in the prediction of hypertension with OR and CI of 10.8527 [4.7434; 24.8305]. e findings of this work suggest that the DL models can effectively advance the prediction of and knowledge about heart failure, but there is a lack of literature regarding DL methods in the field of CVDs. As a result, more DL models should be applied in this field. To confirm our findings, more meta-analysis (e.g., Bayesian network) and thorough research with a larger number of patients are encouraged. 1. Introduction Heart failure and related diseases are the most common cause of death in both males and females in practically all countries around the world [1]. Cardiovascular diseases (CVDs), in particular, are the main cause of death worldwide, accounting for 30% of all fatalities in the United States [2] and 45% in Europe, while costing the European Union €210 billion each year [3]. Despite substantial ad- vances in diagnostic procedures over the last 50 years, cardiologists, primary care physicians, and other healthcare providers face tremendous challenges in the early detection Hindawi Computational Intelligence and Neuroscience Volume 2022, Article ID 5849995, 12 pages https://doi.org/10.1155/2022/5849995