Explainable Heart Disease Diagnosis with Supervised Learning Methods
Abstract The objective of this study is to develop a heart disease diagnosis model with a supervised machine learning algorithm. To that end, random forest (RF), support vector machine (SVM), Naïve Bayes (NB), and extreme boosting (XGBoost) are employed in a medical heart disease dataset to develop a model for heart disease prediction. The performance of the algorithms is investigated and compared for automation of heart disease diagnosis. The best model is selected, and a grid search is applied to improve model performance. The simulation result shows that the XGBoost model outperforms the others, achieving 99.10% accuracy, and receiver operating characteristic curve (AUC score=0.99) compared to RF, SVM, and NB on heart disease detection. Finally, the obtained result is interpreted with Shapley additive model explanation (SHAP) to investigate the effect of each feature on the diagnosis of heart disease. A case study on heart disease diagnosis shows an important insight into the impact of the feature on the diagnosis performance of the supervised learning method. The developed model had an expressively higher prediction accuracy, indicating the utility of supervised learning systems in detecting heart disease in the early stages.
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Singh, A., & Kumar, R. (2020). Heart Disease Prediction Using Machine Learning Algorithms. 2020 International Conference on Electrical and Electronics Engineering (ICE3). https://doi.org/10.1109/ICE348803.2020.9122958
Almutairi, S., et al. (2022). A Context-Aware MRIPPER Algorithm for Heart Disease Prediction. Hindawi Journal of Healthcare Engineering. https://doi.org/10.1155/2022/7853604
Alsaffar, M., et al (2021). Machine Learning for Ischemic heart disease diagnosis aided by Evolutionary Computing. Applied Bionics and Biomechanics. https://doi.org/10.1155/2021/6718029
Assegie, T. A. (2022). Evaluation of the Shapley Additive Explanation Technique for Ensemble Learning Methods. Proceedings of Engineering and Technology Innovation, 21, 20–26. https://doi.org/10.46604/peti.2022.9025
Athanasiou M., et al. (2020). An explainable XGBoost–based approach towards assessing the risk of cardiovascular disease in patients with Type 2 Diabetes Mellitus. 2020 IEEE 20th International Conference on Bioinformatics and Bioengineering (BIBE). https://doi.org/10.1109/BIBE50027.2020.00146
Budholiya, K., Shrivastava, S. K., & Sharma, V. (2022). An optimized XGBOOST based diagnostic system for effective prediction of heart disease. Journal of King Saud University – Computer and Information Sciences, 34(7), 1–10. https://doi.org/10.1016/j.jksuci.2020.10.013
Dissanayake K., & Johar, G. M. (2021). Comparative Study on Heart Disease Prediction Using Feature Selection Techniques on Classification Algorithms. Applied Computational Intelligence and Soft Computing. https://doi.org/10.1155/2021/5581806
Kim, K. H., & Kang, S. (2017). Neural Network-Based Coronary Heart Disease Risk Prediction Using Feature Correlation Analysis. Journal of Healthcare Engineering. https://doi.org/10.1155/2017/2780501
Muhammad, Y., Tahir, M., Hayat, M., & Chong, K. T. (2020). Early and accurate detection and diagnosis of heart disease using intelligent computational model. Scientific Reports, 10(1). https://doi.org/10.1038/s41598-020-76635-9
Nugroho K. S., et al. (2022). Effective predictive modelling for coronary artery diseases using support vector machine. IAES International Journal of Artificial Intelligence, 11(1), 345–355. https://doi.org/10.11591/ijai.v11.i1.pp345-355
Oh, T. I., Kim, D., Lee, S., et al. (2022). Machine learning-based diagnosis and risk factor analysis of cardiocerebrovascular disease based on KNHANES. Scientific Reports, 12(1). https://doi.org/10.1038/s41598-022-06333-1
Porto, R., et al. (2021). Minimum Relevant Features to Obtain Explainable Systems for Predicting Cardiovascular Disease Using the Statlog Data Set. Applied Science. https://doi.org/10.20944/preprints202012.0318.v1
Saboor, A., et al. (2022). A method for Improving Prediction of Human Heart Disease Using Machine Learning Algorithms. Mobile Information Systems. https://doi.org/10.1155/2022/1410169
Shah, D., Patel, S., & Bharti, S. K. (2020). Heart Disease Prediction using Machine Learning Techniques. SN Computer Science, 1. https://doi.org/10.1007/s42979-020-00365-y
Shehzadi, S., et al. (2022). Diagnosis of Chronic Ischemic Heart Disease Using Machine Learning Techniques. Computational Intelligence and Neuroscience. https://doi.org/10.1155/2022/3823350
Singh, A., & Kumar, R. (2020). Heart Disease Prediction Using Machine Learning Algorithms. 2020 International Conference on Electrical and Electronics Engineering (ICE3). https://doi.org/10.1109/ICE348803.2020.9122958
Assegie, T. A., Sushma, S. J., & Mamanazarovna, S. S. (2023). Explainable Heart Disease Diagnosis with Supervised Learning Methods. ADCAIJ: Advances in Distributed Computing and Artificial Intelligence Journal, 12(1), e31228. https://doi.org/10.14201/adcaij.31228
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