Predictive Analytics for Multiple Diseases Using Machine Learning

Vijayalakshmi M., Shiva Subramanian, Harsh Jain

2025

Abstract

The prevalence of chronic diseases is increasing among Medicare patients, and there is a need for innovative ways of healthcare management. Medical practitioners are commonly swamped by the large quantities of information involved in the analysis so that it becomes an uphill task to interpret symptoms and diseases within time. Utilization of supervised ML algorithm has proved its effectiveness for diagnosis purposes to diseases and enables medical professionals to detect risky conditions in an early stage. The goal of the project is to predict the likelihood of a variety of diseases in the Medicare population using data analytics and machine learning. We will identify patterns and risk factors associated with multiple diseases through preprocessing and enhancement of available data. More advanced ML algorithms will be leveraged to create prediction models: SVM for the prediction of both diabetes and Parkinson's disease and logistic regression for heart disease. Feeding labeled input data into the algorithms during the training process will help learn correlations between feature and disease correlations. When predicting, the models will then be tested on an independent data set to establish how accurately they are able to predict outputs and call out potential issues for fine-tuning if needed. Such insights help healthcare providers identify a patient issue earlier and intervene appropriately. This, of course, improves patient outcomes while controlling health costs. This project depicts the promise of predictive analytics with respect to improving patient care under Medicare through creating personalized and proactive healthcare solutions.

Paper Citation

in Harvard Style

M. V., Subramanian S. and Jain H. (2025). Predictive Analytics for Multiple Diseases Using Machine Learning. In Proceedings of the 1st International Conference on Research and Development in Information, Communication, and Computing Technologies - ICRDICCT`25; ISBN 978-989-758-777-1, SciTePress, pages 92-99. DOI: 10.5220/0013923300004919

in Bibtex Style

@conference{icrdicct`2525,
author={Vijayalakshmi M. and Shiva Subramanian and Harsh Jain},
title={Predictive Analytics for Multiple Diseases Using Machine Learning},
booktitle={Proceedings of the 1st International Conference on Research and Development in Information, Communication, and Computing Technologies - ICRDICCT`25},
year={2025},
pages={92-99},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0013923300004919},
isbn={978-989-758-777-1},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 1st International Conference on Research and Development in Information, Communication, and Computing Technologies - ICRDICCT`25
TI - Predictive Analytics for Multiple Diseases Using Machine Learning
SN - 978-989-758-777-1
AU - M. V.
AU - Subramanian S.
AU - Jain H.
PY - 2025
SP - 92
EP - 99
DO - 10.5220/0013923300004919
PB - SciTePress