Cardiac Condition Anticipation and Prognostication via Integrated WOA and Bagging-GBDT
Keywords:
Cardiac disease predication and diagnosis, Machine learning, Bagging-GBDT, WOA, Optimal hyperparameter selectionAbstract
Cardiac disease is a significant health concern that leads to more than 17 million deaths every year. The integration of Internet of Medical Things (IoMT) and Artificial Intelligence (AI) in healthcare has made considerable improvements in patient outcomes. However, the recent approach of using the Bagging-Fuzzy-GBDT classifier for anticipating and prognosticating Cardiac disease may not be suitable for all cases due to membership function-based data fuzzification and Grid Search (GS) based hyperparameter selection limitations. This study presents a novel method combining optimization with ensemble learning techniques, specifically Whale Optimization Algorithm (WOA), with Bagging-GBDT classifiers to anticipate and prognosticate Cardiac disease more effectively. This new approach employs membership functions to capture data uncertainty and vagueness, uses ensemble learning techniques to generate multiple random subsamples from the original dataset, and finally utilizes the Bagging-WOA-GBDT classifier to build an accurate prognostication model based on enhanced data representation. The results of the experiment conducted on a publicly available Cardiac disease dataset show that the proposed approach performs better than traditional classifier methods. It provides more reliable and accurate prognostication for Cardiac disease. These findings suggest that the suggested approach could be a valuable tool for healthcare practitioners in diagnosing and preventing Cardiac disease.
Downloads
References
Azmi, J., Arif, M., Nafis, M. T., Alam, M. A., Tanweer, S., & Wang, G. (2022). A systematic review on machine learning approaches for cardiovascular disease prediction using medical big data. Medical Engineering & Physics, 103825.
Kibria, H. B., & Matin, A. (2022). The severity prediction of the binary and multi-class cardiovascular disease− A machine learning-based fusion approach. Computational Biology and Chemistry, 98, 107672.
Kishor, A., & Chakraborty, C. (2022). AI and internet of things based healthcare 4.0 monitoring system. Wireless personal communications, 127(2), 1615-1631.
Ashfaq, Z., Rafay, A., Mumtaz, R., Zaidi, S. M. H., Saleem, H., Zaidi, S. A. R., ... & Haque, A. (2022). A review of enabling technologies for Internet of Medical Things IoMT Ecosystem. Ain Shams Engineering Journal, 13(4), 101660.
Kibria, H. B., & Matin, A. (2022). The severity prediction of the binary and multi-class cardiovascular disease− A machine learning-based fusion approach. Computational Biology and Chemistry, 98, 107672.
Ajagbe, S. A., Awotunde, J. B., Adesina, A. O., Achimugu, P., & Kumar, T. A. (2022). Internet of Medical Things IoMT: Applications, Challenges, and Prospects in a Data-Driven Technology. Intelligent Healthcare: Infrastructure, Algorithms and Management, 299-319.
El Hechi, M. W., Eddine, S. A. N., Maurer, L. R., &Kaafarani, H. M. (2021). Leveraging interpretable machine learning algorithms to predict postoperative patient outcomes on mobile devices. Surgery, 169(4), 750-754.
Huang, Y. P., Vadloori, S., Chu, H. C., Kang, E. Y. C., Wu, W. C., Kusaka, S., & Fukushima, Y. (2020). Deep learning models for automated diagnosis of retinopathy of prematurity in preterm infants. Electronics, 9(9), 1444.
Zhu, H., Wang, G., Zheng, J., Zhu, H., Huang, J., Luo, E., ... & He, X. (2022). Preoperative prediction for lymph node metastasis in early gastric cancer by interpretable machine learning models: A multicenter study. Surgery, 171(6), 1543-1551.
Elgedawy, M. N. (2017). Prediction of breast cancer using random forest, support vector machines and naïve Bayes. International Journal of Engineering and Computer Science, 6(1), 19884-19889.
Feres, M., Louzoun, Y., Haber, S., Faveri, M., Figueiredo, L. C., & Levin, L. (2018). Support vector machine-based differentiation between aggressive and chronic periodontitis using microbial profiles. International dental journal, 68(1), 39-46.
Lucas, M., Jansen, I., van Leeuwen, T. G., Oddens, J. R., de Bruin, D. M., &Marquering, H. A. (2022). Deep learning–based recurrence prediction in patients with non–muscle-invasive bladder cancer. European Urology Focus, 8(1), 165-172.
Raja, M. S., Anurag, M., Reddy, C. P., &Sirisala, N. R. (2021, January). Machine learning based Heart disease prediction system. In 2021 International Conference on Computer Communication and Informatics (ICCCI) (pp. 1-5). IEEE.
Islam, J., & Zhang, Y. (2017). A novel deep learning based multi-class classification method for Alzheimer’s disease detection using brain MRI data. In Brain Informatics: International Conference, BI 2017, Beijing, China, November 16-18, 2017, Proceedings (pp. 213-222). Springer International Publishing.
Tarawneh, O., Otair, M., Husni, M., Abuaddous, H. Y., Tarawneh, M., &Almomani, M. A. (2022). Breast cancer classification using decision tree algorithms. International Journal of Advanced Computer Science and Applications, 13(4).
Patil, L., Sirsat, A., Kamble, D., & Pawar, M. Y. (2017). Lung cancer detection using decision tree algorithm. International Research Journal of Engineering and Technology, 4(2), 1885-1888.
Sisodia, D., & Sisodia, D. S. (2018). Prediction of diabetes using classification algorithms. Procedia computer science, 132, 1578-1585.
Hosseinpour, M., Ghaemi, S., Khanmohammadi, S., &Daneshvar, S. (2022). A hybrid high‐order type‐2 FCM improved random forest classification method for breast cancer risk assessment. Applied Mathematics and Computation, 424, 127038.
Gorji, H. T., &Haddadnia, J. (2015). A novel method for early diagnosis of Alzheimer’s disease based on pseudo Zernike moment from structural MRI. Neuroscience, 305, 361-371.
Malik, S., Harous, S., & El-Sayed, H. (2021). Comparative analysis of machine learning algorithms for early prediction of diabetes mellitus in women. In Modelling and Implementation of Complex Systems: Proceedings of the 6th International Symposium, MISC 2020, Batna, Algeria, October 24‐26, 2020 6 (pp. 95-106). Springer International Publishing.
Esteva, A., Kuprel, B., Novoa, R. A., Ko, J., Swetter, S. M., Blau, H. M., &Thrun, S. (2017). Dermatologist-level classification of skin cancer with deep neural networks. nature, 542(7639), 115-118.
Qummar, S., Khan, F. G., Shah, S., Khan, A., Shamshirband, S., Rehman, Z. U., ... &Jadoon, W. (2019). A deep learning ensemble approach for diabetic retinopathy detection. Ieee Access, 7, 150530-150539.
Lipton, Z. C., Kale, D. C., Elkan, C., & Wetzel, R. (2015). Learning to diagnose with LSTM recurrent neural networks. arXiv preprint arXiv:1511.03677.
Anggoro, D. A., &Kurnia, N. D. (2020). Comparison of accuracy level of support vector machine (SVM) and K-nearest neighbors (KNN) algorithms in predicting Heart disease. International Journal, 8(5), 1689-1694.
Tayefi, M., Tajfard, M., Saffar, S., Hanachi, P., Amirabadizadeh, A. R., Esmaeily, H., ... & Ghayour-Mobarhan, M. (2017). hs-CRP is strongly associated with coronary Heart disease (CHD): A data mining approach using decision tree algorithm. Computer methods and programs in biomedicine, 141, 105-109.
Jeyaranjani, J., Rajkumar, T. D., & Kumar, T. A. (2021). WITHDRAWN: Coronary Heart disease diagnosis using the efficient ANN model.
Yuan, X., Chen, J., Zhang, K., Wu, Y., & Yang, T. (2021). A stable AI-based binary and multiple class Heart disease prediction model for IoMT. IEEE Transactions on Industrial Informatics, 18(3), 2032-2040.
Mary, M. M. A. (2020). Heart disease prediction using machine learning techniques: A survey. International Journal For Research In Applied Science And Engineering Technology, 8(10), 441-447.
Jiang, H., Mao, H., Lu, H., Lin, P., Garry, W., Lu, H.,.& Chen, X. (2021). Machine learning-based models to support decision-making in emergency department triage for patients with suspected cardiovascular disease. International Journal of Medical Informatics, 145, 104326.
Yuan, X., Chen, J., Zhang, K., Wu, Y., & Yang, T. (2021). A stable AI-based binary and multiple class Heart disease prediction model for IoMT. IEEE Transactions on Industrial Informatics, 18(3), 2032-2040.
Tabrizchi, H., Tabrizchi, M., &Tabrizchi, H. (2020). Breast cancer diagnosis using a multi-verse optimizer-based gradient boosting decision tree. SN Applied Sciences, 2, 1-19.
Yu, Z., Wang, Z., Zeng, F., Song, P., Baffour, B. A., Wang, P., ... & Li, L. (2021). Volcanic lithology identification based on parameter-optimized GBDT algorithm: A case study in the Jilin Oilfield, Songliao Basin, NE China. Journal of Applied Geophysics, 194, 104443.
Yuan, X., Chen, J., Zhang, K., Wu, Y., & Yang, T. (2021). A stable AI-based binary and multiple class Heart disease prediction model for IoMT. IEEE Transactions on Industrial Informatics, 18(3), 2032-2040.
Bisht, K., & Kumar, A. (2023). A method for fuzzy time series forecasting based on interval index number and membership value using fuzzy c-means clustering. Evolutionary Intelligence, 16(1), 285-297.
Louk, M. H. L., & Tama, B. A. (2023). Dual-IDS: A bagging-based gradient boosting decision tree model for network anomaly intrusion detection system. Expert Systems with Applications, 213, 119030.
Mirjalili, S., & Lewis, A. (2016). The whale optimization algorithm. Advances in engineering software, 95, 51-67.
Saidala, R. K., Devarakonda, N., Rao, T. B., Syam, J., & Kumar, S. (2023). A Novel Optimum Clustering Method Using Variant of NOA. In Computational Intelligence in Medical Decision Making and Diagnosis (pp. 221-237). CRC Press.
Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2023 Javvaji Venkatarao, V. Deeban Chakravarthy
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
All papers should be submitted electronically. All submitted manuscripts must be original work that is not under submission at another journal or under consideration for publication in another form, such as a monograph or chapter of a book. Authors of submitted papers are obligated not to submit their paper for publication elsewhere until an editorial decision is rendered on their submission. Further, authors of accepted papers are prohibited from publishing the results in other publications that appear before the paper is published in the Journal unless they receive approval for doing so from the Editor-In-Chief.
IJISAE open access articles are licensed under a Creative Commons Attribution-ShareAlike 4.0 International License. This license lets the audience to give appropriate credit, provide a link to the license, and indicate if changes were made and if they remix, transform, or build upon the material, they must distribute contributions under the same license as the original.
