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) 1-8 https://doi.org/10.25163/angiotherapy.839535
Submitted: 08 January 2024 Revised: 28 February 2024 Published: 03 March 2024
Heart disease, responsible for millions of deaths annually, necessitates early detection. Machine Learning offers efficient, cost-effective diagnostic tools. The proposed Wild Horse Optimizer (WHO) and SVM classifier enhance heart disease forecasting, addressing critical healthcare needs.
Abstract
Background: As one of the leading causes of morbidity and mortality worldwide, the diagnosis of acute cardiovascular disease (acute CVD) in emergency settings remains a significant challenge. Machine learning (ML) is artificial intelligence that allows computer programs to learn from and analyze large data sets without human intervention. As a diagnostic tool, ML presents numerous advantages, such as convenience, speed, and affordability. Methods: This research utilized a new model for predicting heart disease for feature selection(Wild Horse Optimizer, WHO) and another classification model (Support Vector Machine, SVM). In the WHO algorithm, like envisioning social behaviors of wild horses, we observe that wild horses display diverse behaviors like leading, grazing, moving, mating and chase. One odd behavior about wild horses is that young foals will break away from the herd to avoid the breed in its related herd before reaching maturity. It defines the decencies of the horses. Discussion: Multiple ML techniques were employed to train classifiers using the Cleveland heart disease dataset after conducting feature selection using WHO algorithm. The performance of these models was evaluated based on different parameters like Sensitivity, Accuracy, Specificity, and AUC. The SVM classifier model trained using the WHO approach proved better than the existing methods. In the research, we proposed a Wild Horse Optimization algorithm to optimize the features for Heart Disease prediction. After feature optimization, the optimized dataset is then given to Support Vector machine classification, which performs the classification of heart disease. After evaluating different experimental results, authors conclude that combining this feature selection algorithm and SVM as a classifier for forecasting heart disease is very effective. With the help of this approach, we can improve the early detection of heart disease and effectively manage severe heart disease. The experimental result graph shows an increased accuracy rate of the spectra feature subject.
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.
View Dimensions
View Altmetric
Save
Citation
View
Share