Optimizing Machine Learning Classifiers for Enhanced Cardiovascular Disease Prediction
Received: 29 November 2023 | Revised: 24 December 2023 | Accepted: 29 December 2023 | Online: 31 January 2024
Corresponding author: Gamal Saad Mohamed Khamis
Abstract
A key challenge in developing Machine Learning (ML) models for predicting or diagnosing Cardiovascular Disease (CVD), is selecting suitable algorithms and fine-tuning their parameters. In this study, we employed three ML techniques, namely Auto-WEKA, Decision Table/Naive Bayes (DTNB), and Multiobjective Evolutionary (MOE) fuzzy classifier to create diagnostic models using the Heart Disease Dataset from IEEE Dataport. Auto-WEKA generated a highly accurate model with a 100% success rate through optimal classifier selection and hyperparameter configuration. The DTNB classifier yielded a satisfactory 85.63% prediction accuracy concerning patients' risk levels. Further refinements, though, could help reduce possible misclassifications. Finally, the MOE fuzzy classifier achieved approximately 81.6% accuracy, indicating the potential for enhancing precision and recall values by adjusting classifier settings. Our findings underscore the promise of ML tools in CVD diagnosis and suggest further optimization of classifier parameters for superior performance.
Keywords:
Machine Learning (ML), Auto-WEKA, Lazy IBK, DTNB, MOE fuzzy classifier, CVDDownloads
References
M. Armbrust, A. D. Joseph, R. H. Katz, and D. A. Patterson, "Above the clouds : A Berkeley view of cloud computing," Science, vol. 53, no. 4, pp. 50-58, 2010.
A. L. Bui, T. B. Horwich, and G. C. Fonarow, "Epidemiology and risk profile of heart failure," Nature Reviews Cardiology, vol. 8, no. 1, pp. 30-41, Jan. 2011.
B. Trstenjak, D. Donko, and Z. Avdagic, "Adaptable Web Prediction Framework for Disease Prediction Based on the Hybrid Case Based Reasoning Model," Engineering, Technology & Applied Science Research, vol. 6, no. 6, pp. 1212–1216, Dec. 2016.
R. Ramesh and S. Sathiamoorthy, "A Deep Learning Grading Classification of Diabetic Retinopathy on Retinal Fundus Images with Bio-inspired Optimization," Engineering, Technology & Applied Science Research, vol. 13, no. 4, pp. 11248–11252, Aug. 2023.
G. Someshwaran and V. Sarada, "A Research Review on Fetal Heart Disease Detection Techniques," in 2022 6th International Conference on Intelligent Computing and Control Systems (ICICCS), Madurai, India, Feb. 2022, pp. 1674–1681.
M. Liu and Y. Kim, "Classification of Heart Diseases Based On ECG Signals Using Long Short-Term Memory," in 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Honolulu, HI, USA, Jul. 2018, pp. 2707–2710.
M. N. R. Chowdhury, E. Ahmed, Md. A. D. Siddik, and A. U. Zaman, "Heart Disease Prognosis Using Machine Learning Classification Techniques," in 2021 6th International Conference for Convergence in Technology (I2CT), Maharashtra, India, Apr. 2021.
R. Atallah and A. Al-Mousa, "Heart Disease Detection Using Machine Learning Majority Voting Ensemble Method," in 2019 2nd International Conference on new Trends in Computing Sciences (ICTCS), Amman, Jordan, Jul. 2019.
S. Ismaeel, A. Miri, and D. Chourishi, "Using the Extreme Learning Machine (ELM) technique for heart disease diagnosis," in 2015 IEEE Canada International Humanitarian Technology Conference (IHTC2015), Ottawa, ON, Canada, Feb. 2015.
P. Singh, S. Singh and G. S. Pandi-Jain. journal of, and undefined 2018, "Effective heart disease prediction system using data mining techniques," International Journal of Nanomedicine, vol. 13, no. T-NANO 2014 Abstracts, pp. 121-124, 2018.
C.-H. Lin, P.-K. Yang, Y.-C. Lin, and P.-K. Fu, "On Machine Learning Models for Heart Disease Diagnosis," in 2020 IEEE 2nd Eurasia Conference on Biomedical Engineering, Healthcare and Sustainability (ECBIOS), Tainan, Taiwan, May 2020, pp. 158–161.
M. A. Hall and E. Frank, "Combining Naive Bayes and Decision Tables," in Proceedings of Twenty-First International Florida Artificial Intelligence Research Society Conference, Coconut Grove, FL, USA, May 2008, pp. 318–319.
F. Jiménez, G. Sánchez, and J. M. Juárez, "Multi-objective evolutionary algorithms for fuzzy classification in survival prediction," Artificial Intelligence in Medicine, vol. 60, no. 3, pp. 197-219, 2014.
R. Bharti, A. Khamparia, M. Shabaz, G. Dhiman, S. Pande, and P. Singh, "Prediction of heart disease using a combination of machine learning and deep learning," Computational Intelligence and Neuroscience, vol. 2021, 2021, Art. no. 8387680.
A. K. Dubey, A. K. Sinhal, and R. Sharma, "An Improved Auto Categorical PSO with ML for Heart Disease Prediction," Engineering, Technology & Applied Science Research, vol. 12, no. 3, pp. 8567–8573, Jun. 2022.
L. Kotthoff, C. Thornton, H. H. Hoos, F. Hutter, and K. Leyton-Brown, "Auto-WEKA 2.0: Automatic model selection and hyperparameter optimization in WEKA," Journal of Machine Learning Research, vol. 18, pp. 1-5, Mar. 2017.
E. Frank, M. A. Hall, and I. H. Witten, The WEKA Workbench. Online Appendix for "Data Mining: Practical Machine Learning Tools and Techniques," 4th ed. Burlington, NJ, USA: Morgan Kaufmann, 2016.
R. E. Schapire, "Explaining AdaBoost," in Empirical Inference: Festschrift in Honor of Vladimir N. Vapnik, B. Schölkopf, Z. Luo, and V. Vovk, Eds. Berlin, Heidelberg, Germany: Springer, 2013, pp. 37–52.
A. J. Viera and J. M. Garrett, "Understanding interobserver agreement: the kappa statistic," Family Medicine, vol. 37, no. 5, pp. 360–363, Dec. 2005.
Downloads
How to Cite
License
Copyright (c) 2024 Sultan Munadi Alanazi, Gamal Saad Mohamed Khamis
This work is licensed under a Creative Commons Attribution 4.0 International License.
Authors who publish with this journal agree to the following terms:
- Authors retain the copyright and grant the journal the right of first publication with the work simultaneously licensed under a Creative Commons Attribution License that allows others to share the work with an acknowledgement of the work's authorship and initial publication in this journal.
- Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgement of its initial publication in this journal.
- Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) after its publication in ETASR with an acknowledgement of its initial publication in this journal.
