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Integrated Machine Learning Framework for Multi-Disease Prediction and Geolocation-Based Healthcare Recommendations
Subject area: Science,Engineering and Technology · Area of research: Machine Learning
DOI: https://doi.org/10.64388/IREV9I6-1713005
Abstract
Chronic diseases such as diabetes, heart disease, breast cancer, and diabetic retinopathy continue to impose a substantial burden on healthcare systems due to delayed diagnosis and limited post-diagnostic guidance. Many existing digital health platforms focus solely on prediction while neglecting actionable follow-up, such as identifying suitable healthcare providers. This paper presents an integrated web-based machine learning framework that performs multi-disease risk prediction and provides location-based healthcare recommendations. The system supports four disease models: Logistic Regression for diabetes and diabetic retinopathy, and Random Forest classifiers for heart disease and breast cancer. Users manually input clinical parameters, which are preprocessed and evaluated using pre-trained models deployed on a centralized server. Experimental evaluation on publicly available datasets demonstrates classification accuracies of 75.32% for diabetes, 99.50% for diabetic retinopathy, 90.16% for heart disease, and 83.23% for breast cancer. Beyond prediction, the framework incorporates a geolocation module that recommends nearby hospitals and specialists based on the predicted outcome. The results indicate that combining disease prediction with post-prediction guidance improves practical usability, although clinical deployment would require validation on real-world patient data.
Keywords
Disease Prediction, Machine Learning, Healthcare Recommendation, Logistic Regression, Random Forest, Web-Based Healthcare System
How to cite this paper
@article{1713005,
author = {Reya Javaid, Arsalan, Shuraim Shakeel Bhat, B P Chandana},
title = {Integrated Machine Learning Framework for Multi-Disease Prediction and Geolocation-Based Healthcare Recommendations},
journal = {Iconic Research And Engineering Journals},
year = {2025},
volume = {9},
number = {6},
pages = {1406-1409},
issn = {2456-8880},
url = {https://www.irejournals.com/formatedpaper/1713005.pdf},
abstract = {Chronic diseases such as diabetes, heart disease, breast cancer, and diabetic retinopathy continue to impose a substantial burden on healthcare systems due to delayed diagnosis and limited post-diagnostic guidance. Many existing digital health platforms focus solely on prediction while neglecting actionable follow-up, such as identifying suitable healthcare providers. This paper presents an integrated web-based machine learning framework that performs multi-disease risk prediction and provides location-based healthcare recommendations. The system supports four disease models: Logistic Regression for diabetes and diabetic retinopathy, and Random Forest classifiers for heart disease and breast cancer. Users manually input clinical parameters, which are preprocessed and evaluated using pre-trained models deployed on a centralized server. Experimental evaluation on publicly available datasets demonstrates classification accuracies of 75.32% for diabetes, 99.50% for diabetic retinopathy, 90.16% for heart disease, and 83.23% for breast cancer. Beyond prediction, the framework incorporates a geolocation module that recommends nearby hospitals and specialists based on the predicted outcome. The results indicate that combining disease prediction with post-prediction guidance improves practical usability, although clinical deployment would require validation on real-world patient data.},
keywords = {Disease Prediction, Machine Learning, Healthcare Recommendation, Logistic Regression, Random Forest, Web-Based Healthcare System},
month = {December},
doi = {https://doi.org/10.64388/IREV9I6-1713005}
}