Effectiveness of Artificial Intelligence Models for Cardiovascular Disease Prediction: Network Meta-Analysis
Decision trees; Deep learning; Diagnosis; Diseases; Forecasting; Heart; Neural networks; Random processes; Support vector machines; Cardiovascular disease; Causes of death; Confidence interval; Electronic database; Heart failure; Intelligence models; Learning models; Meta-analysis; Odd ratios; Web o...
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my.uniten.dspace-272462023-05-29T17:41:32Z Effectiveness of Artificial Intelligence Models for Cardiovascular Disease Prediction: Network Meta-Analysis Baashar Y. Alkawsi G. Alhussian H. Capretz L.F. Alwadain A. Alkahtani A.A. Almomani M. 56768090200 57191982354 55430817100 6602660867 54895196300 55646765500 56600532600 Decision trees; Deep learning; Diagnosis; Diseases; Forecasting; Heart; Neural networks; Random processes; Support vector machines; Cardiovascular disease; Causes of death; Confidence interval; Electronic database; Heart failure; Intelligence models; Learning models; Meta-analysis; Odd ratios; Web of Science; Cardiology; artificial intelligence; Bayes theorem; cardiovascular disease; female; human; machine learning; male; meta analysis; network meta-analysis; Artificial Intelligence; Bayes Theorem; Cardiovascular Diseases; Female; Humans; Machine Learning; Male; Network Meta-Analysis 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. The 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. The search strategy was performed according to the Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA) statement. The methodological quality of studies was assessed by following the Quality Assessment of Diagnostic Accuracy Studies 2 (QUADAS-2) guidelines. The 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. The 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]. The 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. � 2022 Yahia Baashar et al. Final 2023-05-29T09:41:31Z 2023-05-29T09:41:31Z 2022 Article 10.1155/2022/5849995 2-s2.0-85125881344 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85125881344&doi=10.1155%2f2022%2f5849995&partnerID=40&md5=972c30b9f8983783413ac6af3991f955 https://irepository.uniten.edu.my/handle/123456789/27246 2022 5849995 All Open Access, Gold Hindawi Limited Scopus |
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Decision trees; Deep learning; Diagnosis; Diseases; Forecasting; Heart; Neural networks; Random processes; Support vector machines; Cardiovascular disease; Causes of death; Confidence interval; Electronic database; Heart failure; Intelligence models; Learning models; Meta-analysis; Odd ratios; Web of Science; Cardiology; artificial intelligence; Bayes theorem; cardiovascular disease; female; human; machine learning; male; meta analysis; network meta-analysis; Artificial Intelligence; Bayes Theorem; Cardiovascular Diseases; Female; Humans; Machine Learning; Male; Network Meta-Analysis |
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56768090200 |
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56768090200 Baashar Y. Alkawsi G. Alhussian H. Capretz L.F. Alwadain A. Alkahtani A.A. Almomani M. |
| format |
Article |
| author |
Baashar Y. Alkawsi G. Alhussian H. Capretz L.F. Alwadain A. Alkahtani A.A. Almomani M. |
| spellingShingle |
Baashar Y. Alkawsi G. Alhussian H. Capretz L.F. Alwadain A. Alkahtani A.A. Almomani M. Effectiveness of Artificial Intelligence Models for Cardiovascular Disease Prediction: Network Meta-Analysis |
| author_sort |
Baashar Y. |
| title |
Effectiveness of Artificial Intelligence Models for Cardiovascular Disease Prediction: Network Meta-Analysis |
| title_short |
Effectiveness of Artificial Intelligence Models for Cardiovascular Disease Prediction: Network Meta-Analysis |
| title_full |
Effectiveness of Artificial Intelligence Models for Cardiovascular Disease Prediction: Network Meta-Analysis |
| title_fullStr |
Effectiveness of Artificial Intelligence Models for Cardiovascular Disease Prediction: Network Meta-Analysis |
| title_full_unstemmed |
Effectiveness of Artificial Intelligence Models for Cardiovascular Disease Prediction: Network Meta-Analysis |
| title_sort |
effectiveness of artificial intelligence models for cardiovascular disease prediction: network meta-analysis |
| publisher |
Hindawi Limited |
| publishDate |
2023 |
| _version_ |
1806427934291918848 |
| score |
13.214268 |