Hypokinetic Rigid Syndrome Prognosis using Random Forest Classifiers and Support Vector Machines


  • Vani Hiremani Department of Computer Science and engineering, Symbiosis Institute of Technology, Symbiosis International University (Deemed University) Pune, India
  • Raghavendra M. Devadas Department of Computer Science and Engineering, Gitam School of Technology, GITAM Bengaluru, Karnataka, India
  • Harshal Patil Department of Computer Science and engineering, Symbiosis Institute of Technology, Symbiosis International University (Deemed University) Pune, India
  • Smita Patil School of Engineering, Computer Science Department, Presidency University Bangalore, Karnataka, India
  • Soni Sweta Department of Computer Engineering Mukesh Patel School of Technology Management & Engineering, SVKM's Narsee Monjee Institute of Management Studies (NMIMS) Deem-to be- University
  • Vijeeta Patil Department of computer science and engineering K.L.E. institute of technology, Hubballli


Hypokinetic rigid syndrome, Random Forest Classifiers, Support Vector Machines, Prognosis


Many individuals worldwide experience Hypokinetic Rigid Syndrome (HRS), a condition more prevalent among those aged 50 and above. Despite numerous technological advancements and breakthroughs, early disease diagnosis is still a formidable challenge. This underscores the need for the development of automatic machine learning techniques to aid healthcare professionals in precisely identifying this condition during its initial stages. The primary aim of this research paper is to perform a comprehensive analysis and comparison of contemporary machine learning methods like Support Vector Machine (SVM) and Random Forest (RFM) used for detecting HRS. To assess and determine the most effective and accurate classifier for HRS categorization, this study concentrates on evaluating SVM and RFM on UCI Machine Learning Repository's Parkinson's Data Set. The results indicate that the support vector machine achieved an 84.3% accuracy and a Kappa score of 0.824, while the random forest exhibited an 87.2% accuracy with a Kappa score of 0.82.


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How to Cite

Hiremani, V. ., Devadas, R. M. ., Patil, H. ., Patil, S. ., Sweta, S. ., & Patil, V. . (2024). Hypokinetic Rigid Syndrome Prognosis using Random Forest Classifiers and Support Vector Machines. International Journal of Intelligent Systems and Applications in Engineering, 12(14s), 632–636. Retrieved from https://ijisae.org/index.php/IJISAE/article/view/4710



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