Machine Learning-Driven Body Composition Analysis for Predicting Clinical Outcomes

Authors

  • Pralohith Reddy Chinthalapelly Mayo Clinic, USA. Author
  • Ranjeet Kumar Pilot Company, USA. Author

DOI:

https://doi.org/10.60087/jklst.v4.n3.006

Abstract

This research article explores the application of machine learning techniques in analyzing body composition metrics to predict clinical outcomes. By leveraging advanced algorithms and large datasets, the study aims to improve the accuracy of body composition assessments, which are crucial for personalized medicine and effective treatment plans. The findings suggest a significant correlation between machine learning-derived body composition metrics and various clinical outcomes, demonstrating the potential of these technologies in enhancing healthcare delivery.

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Published

08-09-2025

Issue

Vol. 4 No. 3 (2025): Digital Transformation in Education: Emerging Technologies and Pedagogical Shifts

Section

Articles

License

Copyright (c) 2025 Journal of Knowledge Learning and Science Technology ISSN: 2959-6386 (online)

Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 International License.

©2024 All rights reserved by the respective authors and JKLST.

How to Cite

Chinthalapelly, P. R., & Kumar, R. . (2025). Machine Learning-Driven Body Composition Analysis for Predicting Clinical Outcomes. Journal of Knowledge Learning and Science Technology ISSN: 2959-6386 (online), 4(3), 72-76. https://doi.org/10.60087/jklst.v4.n3.006

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