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Effectiveness of Artificial Intelligence Models for Cardiovascular Disease Prediction: Network Meta-Analysis
Yahia Baashar1, Gamal Alkawsi2, Hitham Alhussian3
1College of Graduate Studies, Universiti Tenaga Nasional (UNITEN), Selangor, Malaysia.
Insights
This study compared artificial intelligence (AI) models for predicting cardiovascular diseases. Deep learning (DL) models show promise for heart failure prediction, while machine learning (ML) models excel in predicting diabetes, stroke, and hypertension.
Area of Science:
- Cardiology
- Medical Informatics
- Artificial Intelligence
Background:
- Heart failure and cardiovascular diseases (CVDs) are leading global causes of mortality.
- Artificial intelligence (AI), including machine learning (ML) and deep learning (DL), is increasingly vital in advancing CVD therapies.
- Existing research highlights the potential of AI in managing complex cardiac conditions.
Purpose of the Study:
- To conduct a network meta-analysis comparing ML and DL models for predicting heart failure, stroke, hypertension, and diabetes.
- To identify the most effective AI algorithms for specific cardiovascular conditions.
- To evaluate the current literature on AI in CVD prediction.
Main Methods:
- A systematic literature search was conducted across five major electronic databases (ScienceDirect, EMBASE, PubMed, Web of Science, IEEE Xplore).
- Study quality was assessed using the QUADAS-2 guidelines, adhering to PRISMA statement standards.
- Random-effects network meta-analysis, subgroup testing, and league tables were employed to analyze data from 17 studies involving 285,213 patients.
Main Results:
- Deep learning (DL) algorithms demonstrated strong performance in heart failure prediction (AUC: 0.843).
- Machine learning (ML) algorithms showed specific strengths: Gradient Boosting Machine (GBM) for heart failure (91.10% accuracy), Artificial Neural Network (ANN) for diabetes (OR: 0.0905), Support Vector Machine (SVM) for stroke (OR: 25.0801), and Random Forest (RF) for hypertension (OR: 10.8527).
Conclusions:
- DL models offer significant potential for advancing heart failure prediction and understanding.
- There is a notable gap in research concerning DL applications in the broader field of cardiovascular diseases (CVDs).
- Further research, including more meta-analyses and studies with larger patient cohorts, is recommended to validate these findings and explore DL's full potential in CVD management.
Abstract:
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.
