Optimization of Heart Disease Prediction using Supervised learning with Hyperparameter Optimization Methods

Farij O. Ehtiba Ehtiba; Suhaila F. Elfaitouri; Haitham S.  Ben Abdelmula; Ali Elghirani

doi:10.65540/jar.v28i2.672

المؤلفون

Farij O. Ehtiba Ehtiba Libyan Academy for Postgraduate Studies
Suhaila F. Elfaitouri Libyan Academy for Postgraduate Studies, School of Basic Sciences, Department of Computer Science -Misurata, Libya
Haitham S. Ben Abdelmula Computer Networks Department, College of Computer Technology – Zawia, Zawia, Libya
Ali Elghirani Faculty of Information Technology, Libyan International Medical University, Benghazi – Libya

DOI:

https://doi.org/10.65540/jar.v28i2.672

الكلمات المفتاحية:

machine Learning، features selection، cardiovascular diseases، Grid search

الملخص

Coronary artery disease prediction is a challenging task in healthcare due to the increasing mortality rate associated with heart disease globally. Various machine learning techniques, such as logistic regression, support vector machine, K-nearest neighbors, and random forests, have been employed to create predictive models for early detection. By fine-tuning the hyperparameters, the accuracy of these models can be significantly enhanced. The study outcomes demonstrated different levels of accuracy for each algorithm, with logistic regression achieving up to 86.13% accuracy, support vector machine up to 85.71%, Random Forest exhibiting superior performance with 91.60% accuracy, and K-Nearest Neighbors emerging as the top performer with 92.44% accuracy. This research underscores the potential of utilizing relatively simple supervised machine learning algorithms to predict heart disease with exceptional accuracy, highlighting their significant utility in healthcare.

المراجع

Singh, A. and R. Kumar. Heart disease prediction using machine learning algorithms. in 2020 international conference on electrical and electronics engineering (ICE3). 2020. IEEE. DOI: https://doi.org/10.1109/ICE348803.2020.9122958

Terrada, O., et al., Supervised machine learning based medical diagnosis support system for prediction of patients with heart disease. Advances in Science, Technology and Engineering Systems Journal, 2020. 5(5): p. 269-277. DOI: https://doi.org/10.25046/aj050533

Yadav, S.S., et al. Automated cardiac disease diagnosis using support vector machine. in 2020 3rd International Conference on Communication System, Computing and IT Applications (CSCITA). 2020. IEEE. DOI: https://doi.org/10.1109/CSCITA47329.2020.9137817

Gupta, A., et al., MIFH: A machine intelligence framework for heart disease diagnosis. IEEE access, 2019. 8: p. 14659-14674. DOI: https://doi.org/10.1109/ACCESS.2019.2962755

Alotaibi, F.S., Implementation of machine learning model to predict heart failure disease. International Journal of Advanced Computer Science and Applications, 2019. 10(6). DOI: https://doi.org/10.14569/IJACSA.2019.0100637

Kavitha, M., et al. Heart disease prediction using hybrid machine learning model. in 2021 6th international conference on inventive computation technologies (ICICT). 2021. IEEE. DOI: https://doi.org/10.1109/ICICT50816.2021.9358597

Chetankumar, Y.S., D.K. Singh, and S. Menaria, A review on: heart disease prediction using data mining. J Emerg Technol Innov Res (JETIR), 2019.

Mohan, S., C. Thirumalai, and G. Srivastava, Effective heart disease prediction using hybrid machine learning techniques. IEEE access, 2019. 7: p. 81542-81554. DOI: https://doi.org/10.1109/ACCESS.2019.2923707

El Hamdaoui, H., et al. A clinical support system for prediction of heart disease using machine learning techniques. in 2020 5th International Conference on Advanced Technologies for Signal and Image Processing (ATSIP). 2020. IEEE. DOI: https://doi.org/10.1109/ATSIP49331.2020.9231760

10. Valarmathi, R. and T. Sheela, Heart disease prediction using hyper parameter optimization (HPO) tuning. Biomedical Signal Processing and Control, 2021. 70: p. 103033. DOI: https://doi.org/10.1016/j.bspc.2021.103033

Shorewala, V., Early detection of coronary heart disease using ensemble techniques. Informatics in Medicine Unlocked, 2021. 26: p. 100655. DOI: https://doi.org/10.1016/j.imu.2021.100655

Morya, R. and S. Singh. Prediction model of heart diseases based on hybrid model. in Journal of Physics: Conference Series. 2022. IOP Publishing. DOI: https://doi.org/10.1088/1742-6596/2327/1/012071

Singh, P., S. Singh, and G.S. Pandi-Jain, Effective heart disease prediction system using data mining techniques. International journal of nanomedicine, 2018. 13(sup1): p. 121-124. DOI: https://doi.org/10.2147/IJN.S124998

Anitha, S. and N. Sridevi, Heart disease prediction using data mining techniques. Journal of analysis and Computation, 2019.

Ray, S., et al. Chi-squared based feature selection for stroke prediction using AzureML. in 2020 Intermountain Engineering, Technology and Computing (IETC). 2020. IEEE. DOI: https://doi.org/10.1109/IETC47856.2020.9249117

Sekulić, A., et al., Random forest spatial interpolation. Remote Sensing, 2020. 12(10): p. 1687. DOI: https://doi.org/10.3390/rs12101687

Boateng, E.Y., J. Otoo, and D.A. Abaye, Basic tenets of classification algorithms K-nearest-neighbor, support vector machine, random forest and neural network: a review. Journal of Data Analysis and Information Processing, 2020. 8(4): p. 341-357. DOI: https://doi.org/10.4236/jdaip.2020.84020

Xiong, L. and Y. Yao, Study on an adaptive thermal comfort model with K-nearest-neighbors (KNN) algorithm. Building and Environment, 2021. 202: p. 108026. DOI: https://doi.org/10.1016/j.buildenv.2021.108026

Das, A., Logistic regression, in Encyclopedia of Quality of Life and Well-Being Research. 2024, Springer. p. 3985-3986. DOI: https://doi.org/10.1007/978-3-031-17299-1_1689

Schober, P. and T.R. Vetter, Logistic regression in medical research. Anesthesia & Analgesia, 2021. 132(2): p. 365-366. DOI: https://doi.org/10.1213/ANE.0000000000005247

Cervantes, J., et al., A comprehensive survey on support vector machine classification: Applications, challenges and trends. Neurocomputing, 2020. 408: p. 189-215. DOI: https://doi.org/10.1016/j.neucom.2019.10.118

Yang, L. and A. Shami, On hyperparameter optimization of machine learning algorithms: Theory and practice. Neurocomputing, 2020. 415: p. 295-316. DOI: https://doi.org/10.1016/j.neucom.2020.07.061

Manu Siddhartha. (2020). Heart Disease Dataset (Comprehensive). IEEE Dataport. https://dx.doi.org/10.21227/dz4t-cm36