Wild Horse Optimizer and Support Vector Machine (SVM) Classifier Predicts The Heart Disease Converging Nature-Motivated Optimization and Machine Learning
Vishwanadham Mandala 1*, Srinivas Naveen Dolu Surabhi 2, V. R. Balaji 3, Dipak Raghunath Patil 4, Saiyed Faiayaz Waris 5, G. Shobana 6
Journal of Angiotherapy 8(3) 1-12 https://doi.org/10.25163/angiotherapy.839535
Submitted: 08 January 2024 Revised: 28 February 2024 Published: 03 March 2024
Abstract
Background: Heart disease is one of the most known and deadly diseases in the world and many people lose their lives from this disease every year. Early detection of this disease is vital to save people’s lives. Machine Learning (ML), an artificial intelligence technology, is one of the most convenient, fastest, and low-cost ways to detect disease. Methods: This research work, presented a Wild Horse Optimizer (WHO) based feature selection and Support Vector Machine (SVM) developed a classifier for the forecasting of data related to heart diseases. The WHO algorithm that draws inspiration from the social behaviours of wild horses is presented in this work. Horses typically reside in groups consisting of a stallion, numerous mares, and young foals. Horses can be seen engaging in a variety of behaviours, including leading, grazing, chasing, and mating. The interesting quality that sets horses apart from other animals is their kindness. When a horse is decent, before they reach maturity, its foals break away from the herd and join different groups. To avoid the father mating with the siblings or daughter, this separation occurred. The horse's decent behavior served as the primary source of inspiration for the suggested algorithm. Discussion: The models were created by using several ML techniques to train the feature-selected Cleveland heart disease dataset were evaluated and their results were compared. The parameters like Sensitivity, Accuracy, Specificity, and Area under Curve of the SVM classifier model are trained on the dataset utilizing the WHO approach which yields better results when compared with the other existing approaches. Conclusion: According to the findings, the wild horse optimization algorithm and SVM classifier combo performs best when used to forecast heart disease.
Keyword: Machine Learning, Wild Horse Optimization Algorithm (WHO), Support Vector Machine (SVM).
References
A. Saboor, M. Usman, S. Ali, A. Samad, M.F. Abrar, N. Ullah, A method for improving prediction of human heart disease using machine learning algorithms, Mob. Inf. Syst. 2022 (2022).
Anbarasi, M., Anupriya, E., & Iyengar, N. C. S. N. (2010). Enhanced prediction of heart disease with feature subset selection using genetic algorithm. International Journal of Engineering Science and Technology, 2(10), 5370-5376.
C. Gupta, A. Saha, N.S. Reddy, U.D. Acharya, Cardiac disease prediction using supervised machine learning techniques., in: J. Phys. Conf. Ser., 2161 (1) (2022) 012013.
C. Zhou, A. Wieser, Jaccard analysis and LASSO-based feature selection for location fingerprinting with limited computational complexity, in: Progress in Location Based Services 2018 14, Springer, 2018, pp. 71–87.
C.D. Fernando, P.T. Weerasinghe, C.K. Walgampaya, Heart disease risk identification using machine learning techniques for a highly imbalanced dataset: A comparative study, KDU J. Multidiscip. Stud. 4 (2) (2022).
D. Nitanta, R. Priyab, Predicting heart disease using machine learning, Turk. J. Comput. Math. Educ. 12 (13) (2021) 370–376.
E.A. Ogundepo, W.B. Yahya, Performance analysis of supervised classification models on heart disease prediction, Innov. Syst. Softw. Eng. (2023) 1–16.
K. Kanagarathinam, D. Sankaran, R. Manikandan, Machine learning-based risk prediction model for cardiovascular disease using a hybrid dataset, Data Knowl. Eng. 140 (2022) 102042.
K.V.V. Reddy, I. Elamvazuthi, A.A. Aziz, S. Paramasivam, H.N. Chua, S. Pranavanand, An efficient prediction system for coronary heart disease risk using selected principal components and hyperparameter optimization, Appl. Sci. 13 (1) (2023) 118.
Katarya, R., & Meena, S. K. (2021). Machine learning techniques for heart disease prediction: a comparative study and analysis. Health and Technology, 11, 87-97.
Kumar, M. N., Koushik, K. V. S., & Deepak, K. (2018). Prediction of heart diseases using data mining and machine learning algorithms and tools. International Journal of Scientific Research in Computer Science, Engineering and Information Technology, 3(3), 887-898.
M. Ozcan, S. Peker, A classification and regression tree algorithm for heart disease modeling and prediction, Healthc. Anal. 3 (2023) 100130.
Maheswari, S., & Pitchai, R. (2019). Heart disease prediction system using decision tree and naive Bayes algorithm. Current Medical Imaging, 15(8), 712-717.
Mahmoodi, M. S. (2017). Designing a heart disease prediction system using support vector machine. Journal of Health and Biomedical Informatics, 4(1), 1-10.
Mythili, T., Mukherji, D., Padalia, N., & Naidu, A. (2013). A heart disease prediction model using SVM-decision trees-logistic regression (SDL). International Journal of Computer Applications, 68(16).
Naruei, I., & Keynia, F. (2022). Wild horse optimizer: A new meta-heuristic algorithm for solving engineering optimization problems. Engineering with computers, 38(Suppl 4), 3025-3056.
P. Ghosh, S. Azam, M. Jonkman, A. Karim, F.J.M. Shamrat, E. Ignatious, S. Shultana, A.R. Beeravolu, F. De Boer, Efficient prediction of cardiovascular disease using machine learning algorithms with relief and LASSO feature selection techniques, IEEE Access 9 (2021) 19304–19326.
Palaniappan, S., & Awang, R. (2008, March). Intelligent heart disease prediction system using data mining techniques. In 2008 IEEE/ACS international conference on computer systems and applications (pp. 108-115). IEEE.
Pattekari, S. A., & Parveen, A. (2012). Prediction system for heart disease using Naïve Bayes. International journal of advanced computer and mathematical sciences, 3(3), 290-294.
R. Bharti, A. Khamparia, M. Shabaz, G. Dhiman, S. Pande, P. Singh, Prediction of heart disease using a combination of machine learning and deep learning, Comput. Intell. Neurosci. 2021 (2021).
R.C. Das, M.C. Das, M.A. Hossain, M.A. Rahman, M.H. Hossen, R. Hasan, Heart disease detection using ML, in: 2023 IEEE 13th Annual Computing and Communication Workshop and Conference, CCWC, IEEE, 2023, pp. 0983–0987.
Rajdhan, A., Agarwal, A., Sai, M., Ravi, D., & Ghuli, P. (2020). Heart disease prediction using machine learning. INTERNATIONAL JOURNAL OF ENGINEERINGRESEARCH & TECHNOLOGY (IJERT), 9(O4).
Rani, P., Kumar, R., Ahmed, N. M. S., & Jain, A. (2021). A decision support system for heart disease prediction based upon machine learning. Journal of Reliable Intelligent Environments, 7(3), 263-275.
Reddy, K. V. V., Elamvazuthi, I., Aziz, A. A., Paramasivam, S., Chua, H. N., & Pranavanand, S. (2021). Heart disease risk prediction using machine learning classifiers with attribute evaluators. Applied Sciences, 11(18), 8352.
Reddy, N. S. C., Nee, S. S., Min, L. Z., & Ying, C. X. (2019). Classification and feature selection approaches by machine learning techniques: Heart disease prediction. International Journal of Innovative Computing, 9(1).
S. Chikhi, S. Benhammada, Reliefmss: A variation on a feature ranking relieff algorithm, Int. J. Bus. Intell. Data Min. 4 (3–4) (2009) 375–390.
Saxena, K., & Sharma, R. (2016). Efficient heart disease prediction system. Procedia Computer Science, 85, 962-969.
Shah, D., Patel, S., & Bharti, S. K. (2020). Heart disease prediction using machine learning techniques. SN Computer Science, 1, 1-6.
Soni, J., Ansari, U., Sharma, D., & Soni, S. (2011). Predictive data mining for medical diagnosis: An overview of heart disease prediction. International Journal of Computer Applications, 17(8), 43-48.
Suthaharan, S., & Suthaharan, S. (2016). Support vector machine. Machine learning models and algorithms for big data classification: thinking with examples for effective learning, 207-235.
V. Chang, V.R. Bhavani, A.Q. Xu, M. Hossain, An artificial intelligence model for heart disease detection using machine learning algorithms, Healthc. Anal. 2 (2022) 100016.
Zulkiflee, N. F., & Rusiman, M. S. (2021). Heart Disease Prediction Using Logistic Regression. Enhanced Knowledge in Sciences and Technology, 1(2), 177-184.
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