Parameter reduction without performance degradation is a promising task in decision-making problems. For instance, a great challenge exists in constructing cost functions in gaming theory. Nevertheless, soft set theory handles all its drawbacks conveniently through a new tool for the choice function mathematically. In this paper, we propose an algorithm (SSPRDM) for parameter reduction of soft sets through discernibility matrices, and it is implemented in detecting the risk factor of heart disease problems by using six types of machine learning techniques. The parameters are extracted from the heart disease patient data by the SSPRDM algorithm, and then six machine learning techniques (LDA, KNN, SVM, CART, NB, RF) are performed in the prediction of risk factors for heart disease. The experimental results showed that the present hybrid approach provides an accuracy of 88.46% in the Random Forest technique, whereas the same Random Forest classifier provides an accuracy of 69.23% in the prediction of risk factors of cardiovascular disease (CVD) diagnosis in the earlier work which is a drastic improvement. Moreover, out of 18 parameter reductions, the core component is identified as Total Cholesterol, which is to be considered in all types of CVD diagnosis, whereas Sugar-Fasting (C), Total-Cholesterol (G), and HDL-Cholesterol (I) are the core components identified in three parameter reductions ABCEGHI, ACFGIJ, and BCFGIJK.

• Abirami, R. S., & Kumar, G. S. (2022). Comparative study based on analysis of coronavirus disease (COVID-19) detection and prediction using machine learning models. SN Computer Science, 3(1), Article 79. https://doi.org/10.1007/s42979-021-00965-2

• AlArjani, A., Nasseef, M. T., Kamal, S. M., Rao, B. V. S., Mahmud, M., & Uddin, M. S. (2022). Application of mathematical modeling in prediction of COVID-19 transmission dynamics. Arabian Journal for Science and Engineering, 47(8), 10163-10186. https://doi.org/10.1007/s13369-021-06419-4

• Ali, M. I. (2012). Another view on reduction of parameters in soft sets. Applied Soft Computing, 12(6), 1814-1821. https://doi.org/10.1016/j.asoc.2012.01.002

• Alotaibi, A. S. (2022). Hybrid model based on relief algorithm and k-nearest neighbor for erythemato-squamous diseases forecasting. Arabian Journal for Science and Engineering, 47(2), 1299-1307. URL https://doi.org/10.1007/s13369-021-05921-z

• Ansari, G. A., Bhat, S. S., Ansari, M. D., Ahmad, S., Nazeer, J., & Eljialy, A. E. M. (2023). Performance evaluation of machine learning techniques (MLT) for heart disease prediction. Computational and Mathematical Methods in Medicine, 2023, Article 8191261. https://doi.org/10.1155/2023/8191261

• Bhat, S. S., Selvam, V., Ansari, G. A., Ansari, M. D., & Rahman, M. H. (2022). Prevalence and early prediction of diabetes using machine learning in North Kashmir: A case study of District Bandipora. Computational Intelligence and Neuroscience, 2022, Article 2789760. https://doi.org/10.1155/2022/2789760

• Boukhatem, C., Youssef, H. Y., & Nassif, A. B. (2022). Heart disease prediction using machine learning. In 2022 Advances in Science and Engineering Technology International Conferences (ASET) pp. 1-6. IEEE Publishing. https://doi.org/10.1109/ASET53988.2022.9734880

• Cagman, N., & Enginoglu, S. (2010). Soft set theory and uni-int decision making. European Journal of Operational Research, 207(2), 848-855. https://doi.org/10.1016/j.ejor.2010.05.004

• Chen, D., Tsang, E. C. C., Yeung, D. S., & Wang, X. (2005). The parameterization reduction of soft sets and its applications. Computers & Mathematics with Applications, 49(5), 757-763. https://doi.org/10.1016/j.camwa.2004.10.036

• Gupta, G., Gupta, A., Jaiswal, V., & Ansari, M. D. (2018). A review and analysis of mobile health applications for alzheimer patients and caregivers. In 2018 Fifth International Conference on Parallel, Distributed and Grid Computing (PDGC) (pp. 171-175). IEEE Publishing. https://doi.org/10.1109/PDGC.2018.8745995

• Herawan, T., Rose, A. N. M., & Deris, M. M. (2009). Soft set theoretic approach for dimensionality reduction. In D. Ślęzak, T. Kim, Y. Zhang, J. Ma & K. Chung (Eds.), Database Theory and Application (pp. 171-178). Springer. https://doi.org/10.1007/978-3-642-10583-8_20

• Jindal, H., Agrawal, S., Khera, R., Jain, R., & Nagrath, P. (2021). Heart disease prediction using machine learning algorithms. In IOP conference series: materials science and engineering (Vol. 1022, No. 1, p. 012072). IOP Publishing. https://doi.org/10.1088/1757-899X/1022/1/012072

• Kannan, K., & Menaga, A. (2022). Some efficient algorithms on the parameter reduction of soft sets for decision making problems. Proceedings of the National Academy of Sciences, India Section A: Physical Sciences, 92(3), 319-329. https://doi.org/10.1007/s40010-021-00730-3

• Khan, A., & Zhu, Y. (2019). An improved algorithm for normal parameter reduction of soft set. Journal of Intelligence and Fuzzy Systems, 37(2), 2953-2968. https://doi.org/10.3233/JIFS-190071

• Kong, Z., Gao, L., Wang, L., & Li, S. (2008). The normal parameter reduction of soft sets and its algorithm. Computers and Mathematics with Applications, 56(12), 3029-3037. https://doi.org/10.1016/j.camwa.2008.07.013

• Kong, Z., Zhang, G. D., & Wang, L. F. (2014). Normal parameter reduction in soft set based on harmony search algorithm. Applied Mechanics and Materials, 644-650, 2173-2176. https://doi.org/10.4028/www.scientific.net/AMM.644-650.2173

• Kovkov, D. V., Kolbanov, V. M., & Molodtsov, D. A. (2007). Soft sets theory-based optimization. Journal of Computer and Systems Sciences International, 46(6), 872-880. https://doi.org/10.1134/S1064230707060032

• Ma, X., Sulaiman, N., Qin, H., Herawan, T., & Zain, J. M. (2011). A new efficient normal parameter reduction algorithm of soft sets. Computer & Mathematics with Applications, 62(2), 588-598. https://doi.org/10.1016/j.camwa.2011.05.038

• Maji, P. K., Roy, A. R., & Biswas, R. (2002). An application of soft sets in a decision making problem. Computers & Mathematics with Applications, 44(8), 1077-1083.

• Mezzatesta, S., Torino, C., Meo, P. D., Fiumara, G., & Vilasi, A. (2019). A machine learning-based approach for predicting the outbreak of cardiovascular diseases in patients on dialysis. Computer Methods and Programs in Biomedicine, 177, 9-15. https://doi.org/10.1016/j.cmpb.2019.05.005

• Mitchell, T. M. (1997). Machine Learning. McGraw-Hill.

• Mohammed, M. A. T., Sadiq, A. S., Arshah, R. A., Ernawan, F., & Mirjalili, S. M. (2017). Soft set decision/forecasting system based on hybrid parameter reduction algorithm. Journal of Telecommunication, Electronic and Computer Engineering, 9(2-7), 143-148.

• Molodtsov, D. (1999). Soft set theory - First results. Computers & Mathematics with Applications, 37(4-5), 19-31. https://doi.org/10.1016/S0898-1221(99)00056-5

• Muhammad, L. J., Al-Shourbaji, I., Haruna, A. A., Mohammed, I. A., Ahmad, A., & Jibrin, M. B. (2021). Machine learning predictive models for coronary artery disease. SN Computer Science, 2(5), Article 350. https://doi.org/10.1007/s42979-021-00731-4

• Palaniappan, S., Raghavi, V., David, B., & Nisha, S. P. (2021). Prediction of epidemic disease dynamics on the infection risk using machine learning algorithms. SN Computer Science, 3(1), Article 47. https://doi.org/10.1007/s42979-021-00902-3

• Pawlak, Z. (1982). Rough sets. International Journal of Computer & Information Sciences, 11(5), 341-356. https://doi.org/10.1007/BF01001956

• Pawlak, Z. (1991). Rough Sets: Theoretical Aspects of Reasoning about Data (Vol. 9). Springer Science & Business Media.

• Rahman, M. M., Islam, M. M., Manik, M. M. H., Islam, M. R., & Al-Rakhami, M. S. (2021). Machine learning approaches for tackling novel Coronavirus (COVID-19) pandemic. SN Computer Science, 2(5), Article 384. https://doi.org/10.1007/s42979-021-00774-7

• Rose, A. N. M., Herawan, T., & Deris, M. M. (2010). A framework of decision making based on maximal supported sets. In L. Zhang, B. L. Lu & J. Kwok (Eds.), Advances in Neural Networks - ISNN 2010 (pp. 473-482). Springer. https://doi.org/10.1007/978-3-642-13278-0_61

• Sadiq, A. S., Tahir, M. A., Ahmed, A. A., & Alghushami, A. (2020). Normal parameter reduction algorithm in soft set based on hybrid binary particle swarm and biogeography optimizer. Neural Computing and Applications, 32(16), 12221-12239. https://doi.org/10.1007/s00521-019-04423-2

• Shah, D., Patel, S., & Bharti, S. K. (2020). Heart disease prediction using machine learning techniques. SN Computer Science, 1(6), Article 345. https://doi.org/10.1007/s42979-020-00365-y

• Sharma, D., Gotlieb, N., Farkouh, M. E., Patel, K., Xu, W., & Bhat, M. (2022). Machine learning approach to classify cardiovascular disease in patients with nonalcoholic fatty liver disease in the UK Biobank Cohort. Journal of the American Heart Association, 11(1), Article e022576. https://doi.org/10.1161/JAHA.121.022576

• Wang, S. H., Wu, K., Chu, T., Fernandes, S. L., Zhou, Q., Zhang, Y. D., & Sun, J. (2021). SOSPCNN: Structurally optimized stochastic pooling convolutional neural network for tetralogy of fallot recognition. Wireless Communications and Mobile Computing, 2021, Article 5792975. https://doi.org/10.1155/2021/5792975

• Xie, N. X. (2016). An algorithm on the parameter reduction of soft sets. Fuzzy Information and Engineering, 8(2), 127-145. https://doi.org/10.1016/j.fiae.2016.06.001

• Yuksel, S., Dizman, T., Yildizdan, G., & Sert, U. (2013). Application of soft sets to diagnose the prostate cancer risk. Journal of Inequalities and Applications, 2013(1), Article 229. https://doi.org/10.1186/1029-242X-2013-229

• Zhang, Y., Lu, S., Zhou, X., Yang, M., Wu, L., Liu, B., Phillips, P., & Wang, S. (2016). Comparison of machine learning methods for stationary wavelet entropy-based multiple sclerosis detection: Decision tree, k-nearest neighbors, and support vector machine. Simulation, 92(9), 861-871. https://doi.org/10.1177/0037549716666962