Optimal Squirrel Search-Gradient Decision Tree for Cardiovascular Disease Risk Prediction Using Machine Learning

Authors

  • R. Shanthi Department of Computer Applications, B.S Abdur Rahman Crescent Institute of Science and Technology , Vandalur, Tamil Nadu 600048, India
  • M. Shanthi Department of Information Technology, R.M.K Engineering College, Kavaraipettai, Tamil Nadu- 601206, India
  • P.Mohan Kumar School of Computer science and Engineering VIT university Vellore, Vellore, Tamil Nadu 632014
  • Bharath Srinivasaiah Engineer Lead Sr., Elevance Health Inc, Richmond VA-23230.
  • N. Herald Anantha Rufus Department of Electronics and Communication Engineering, Vel Tech Rangarajan Dr Sagunthala,R & D Institute of Science and Technology, Chennai-600062, Tamil Nadu, India
  • S. Balaganesh Assistant Professor, Department of Computer Science and Engineering, P.S.R Engineering college, Sivakasi, Tamil Nadu 626140,India.

Keywords:

Cardiovascular disease prediction, Optimal Squirrel Search-Gradient Decision Tree, NLPF, Big data, Internet of Things, RFSA, machine learning

Abstract

In recent years, big data usage in the Internet of Things (IoT) has rapidly advanced in the medical field. Furthermore, employing big data and IoT for predicting cardiovascular disease (CVD) enables more precise and timely detection of potential risks, thereby enhancing prevention and treatment approaches. Thereafter, cardiovascular diseases can be predicted by utilizing big data in IoT to create personalized and proactive interventions. Nevertheless, acquiring medical information for early disease detection and assigning timely treatment is essential. Furthermore, more prevalent forms of heart disease can impact the heart's blood flow and result in heart attacks. However, accurately predicting heart disease in medical data analysis presents a significant challenge. So to solve this problem, we proposed an Optimal Squirrel Search-Gradient Decision Tree (OSSGDT) method to classify and accurately predict CVD's high-level and low-level risk factors. Moreover, the pre-processing method utilizes a Normalized Low Pass Filter (NLPF) to eliminate noise and enhance the quality of the smooth areas in the image. Furthermore, the Fuzzy Centroid Cluster Means (FCCM) algorithm can be utilized to iteratively update cluster centres for image segmentation. Furthermore, the Relief Feature Selection Algorithm (RFSA) can be utilized to estimate the weight of each feature. Finally, the OSSGDT method based on Machine Learning (ML) techniques can predict high and low-risk factors for CVD. Moreover, using the proposed OSSGDT method, we can evaluate the performance of heart disease prediction based on accuracy, recall, precision, false ratio, and sensitivity.

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Published

24.03.2024

How to Cite

Shanthi, R. ., Shanthi, M., Kumar, P. ., Srinivasaiah, B. ., Rufus, N. H. A. ., & Balaganesh, S. (2024). Optimal Squirrel Search-Gradient Decision Tree for Cardiovascular Disease Risk Prediction Using Machine Learning. International Journal of Intelligent Systems and Applications in Engineering, 12(20s), 909–918. Retrieved from https://ijisae.org/index.php/IJISAE/article/view/5318

Issue

Vol. 12 No. 20s (2024)

Section

Research Article

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