Research Article An Intelligent and Reliable Hyperparameter Optimization Machine Learning Model for Early Heart Disease Assessment Using Imperative Risk Attributes Syed Immamul Ansarullah , 1 Syed Mohsin Saif , 2 Syed Abdul Basit Andrabi, 3 Sajadul Hassan Kumhar, 4 Mudasir M. Kirmani, 5 and Dr. Pradeep Kumar 6 1 Lecturer at the Department of Computer Science, Govt. Degree College Sumbal, J&K, India 2 Research Coordinator at KWINTECH-R LABS (V), Kwintech-Rlabs(V), J&K, India 3 Research Scholar at the Department of Computer Science, Hyderabad, India 4 Research Scholar at the Department of Computer Science, Sehore, India 5 Assistant Professor at the Department of Computer Science, Division of Social Science, FoFy, SKAUST-Kashmir, Srinagar, India 6 Professor at the Department of Computer Science and Information Technology, MANUU, Hyderabad, India Correspondence should be addressed to Syed Immamul Ansarullah; syedansr@gmail.com Received 7 February 2022; Revised 4 March 2022; Accepted 7 March 2022; Published 12 April 2022 Academic Editor: Suneet Kumar Gupta Copyright © 2022 Syed Immamul Ansarullah et al. is is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Heart disease is a severe disorder, which inflicts an adverse burden on all societies and leads to prolonged suffering and disability. We developed a risk evaluation model based on visible low-cost significant noninvasive attributes using hyperparameter op- timization of machine learning techniques. e multiple set of risk attributes is selected and ranked by the recursive feature elimination technique. e assigned rank and value to each attribute are validated and approved by the choice of medical domain experts. e enhancements of applying specific optimized techniques like decision tree, k-nearest neighbor, random forest, and support vector machine to the risk attributes are tested. Experimental results show that the optimized random forest risk model outperforms other models with the highest sensitivity, specificity, precision, accuracy, AUROC score, and minimum misclas- sification rate. We simulate the results with the prevailing research; they show that it can do better than the existing risk assessment models with exceptional predictive accuracy. e model is applicable in rural areas where people lack an adequate supply of primary healthcare services and encounter barriers to benefit from integrated elementary healthcare advances for initial pre- diction. Although this research develops a low-cost risk evaluation model, additional research is needed to understand newly identified discoveries about the disease. 1. Introduction Heart disease is a growing socioeconomic and public health problem with significant mortality figures and disabilities [1]. e British Heart Foundation (BHF) and the Australian Bureau of Statistics (ABS) reported that heart disease causes 26% of all deaths in the United Kingdom and 33.7% of total deaths in Australia [2–6]. e Economic and Social Com- mission of Asia and the Pacific (ESCAP 2010) reports that 1/ 5th of Asian countries are afflicted with noncommunicable diseases like cancer, heart diseases, and chronic respiratory diseases [7]. e cost and mortality transformed heart disease into an epidemic worldwide. For example, the healthcare reports of the British, USA, and China show that heart disease per year in the UK is 9 billion pounds, 312.6 billion dollars in the USA, and 40 billion dollars in China. ese reports show that the heart disease epidemic has a considerable effect on the world and is one of the dominant health and develop- ment challenges in terms of the human suffering they induce Hindawi Journal of Healthcare Engineering Volume 2022, Article ID 9882288, 12 pages https://doi.org/10.1155/2022/9882288