Multiple Health Disease Prediction

Authors

  • Anusha Benchalli Student, Department of Information Science and Engineering, Basaveshwar Engineering College, Bagalkote, India
  • Apurva Jigajinni Student, Department of Information Science and Engineering, Basaveshwar Engineering College, Bagalkote, India
  • Pragati Sawkar Student, Department of Information Science and Engineering, Basaveshwar Engineering College, Bagalkote, India
  • Prajwal Trivedi Student, Department of Information Science and Engineering, Basaveshwar Engineering College, Bagalkote, India
  • Sadhana P. Bangarashetti Professor, Department of Information Science and Engineering, Basaveshwar Engineering College, Bagalkote, India

DOI:

https://doi.org/10.5281/zenodo.11296554

Keywords:

Machine Learning, SVM, Random Forest, Gradient Boosting

Abstract

Predicting multiple health diseases using machine learning (ML) has become increasingly vital for early diagnosis and intervention. The proposed solution focuses on developing robust and accurate predictive models that can simultaneously forecast the likelihood of multiple health conditions based on individual patient data. The dataset comprises diverse health parameters, including lifestyle factors, and clinical indicators.

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Published

25-05-2024

Issue

Vol. 7 No. 5 (2024)

Section

Articles

License

Copyright (c) 2024 Anusha Benchalli, Apurva Jigajinni, Pragati Sawkar, Prajwal Trivedi, Sadhana P. Bangarashetti

Creative Commons License

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

How to Cite

[1]
A. Benchalli, A. Jigajinni, P. Sawkar, P. Trivedi, and S. P. Bangarashetti, “Multiple Health Disease Prediction”, IJRESM, vol. 7, no. 5, pp. 161–167, May 2024, doi: 10.5281/zenodo.11296554.