Ahmed, Marzia and Mohd Herwan, Sulaiman and Hassan, Md Maruf and Bhuiyan, Touhid (2025) Predicting the classification of heart failure patients using optimized machine learning algorithms. IEEE Access, 13. 30555 -30569. ISSN 2169-3536. (Published)
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Abstract
Heart failure is a critical condition with a high mortality rate, making accurate survival prediction essential for timely interventions. This study proposes an optimized machine learning approach using Gradient Boosting Machine (GBM) and Adaptive Inertia Weight Particle Swarm Optimization (AIWPSO) to predict heart failure survival. The dataset, sourced from Kaggle, includes clinical features such as age, ejection fraction, and serum creatinine levels for 299 heart failure patients. To address the imbalance in survival outcomes, Synthetic Minority Over-sampling Technique (SMOTE) was employed to balance the dataset, followed by SelectKBest and Chi-square feature selection methods to retain the most significant predictors. The optimized hyperparameters for the GBM model were identified using the AIW-PSO algorithm, which effectively balanced exploration and exploitation by adaptively adjusting inertia weights. Model selection was further refined using information criteria, including Akaike Information Criterion (AIC) and Bayesian Information Criterion (BIC), ensuring that the best-performing model was chosen based on both predictive accuracy and model complexity. The optimized GBM model achieved a test accuracy of 94%, demonstrating superior performance compared to traditional machine learning models. The study underscores the importance of hyperparameter tuning through metaheuristic algorithms and highlights the potential of AIW-PSO in enhancing model performance for clinical prediction tasks. These findings have significant implications for clinical decision-making, offering a reliable and interpretable tool for predicting patient outcomes in heart failure management.
| Item Type: | Article |
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| Additional Information: | Indexed by Scopus |
| Uncontrolled Keywords: | AIW-PSO optimization; Class imbalance handling; Heart failure survival prediction; Hyperparameter optimization; Machine learning algorithms |
| Subjects: | T Technology > TK Electrical engineering. Electronics Nuclear engineering |
| Faculty/Division: | Institute of Postgraduate Studies Faculty of Electrical and Electronic Engineering Technology |
| Depositing User: | Mrs. Nurul Hamira Abd Razak |
| Date Deposited: | 11 Mar 2025 05:06 |
| Last Modified: | 11 Mar 2025 05:06 |
| URI: | http://umpir.ump.edu.my/id/eprint/44038 |
| Download Statistic: | View Download Statistics |
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