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SmartHealth: A Web-Integrated Machine Learning Framework for Real-Time Type 2 Diabetes Prediction
| International Journal of Computer Applications |
| Foundation of Computer Science (FCS), NY, USA |
| Volume 187 - Number 69 |
| Year of Publication: 2025 |
| Authors: Balogun Temitayo E., Ogundumila Olanrewaju A., Aderiye Daniel A., Ekundayo M. Omotehinse |
10.5120/ijca2025925992
|
Balogun Temitayo E., Ogundumila Olanrewaju A., Aderiye Daniel A., Ekundayo M. Omotehinse . SmartHealth: A Web-Integrated Machine Learning Framework for Real-Time Type 2 Diabetes Prediction. International Journal of Computer Applications. 187, 69 ( Dec 2025), 1-6. DOI=10.5120/ijca2025925992
Abstract
Diabetes mellitus, especially Type 2 diabetes, is a major global health challenge with increasing prevalence and serious complications if not detected early. Early prediction of individuals at risk is therefore essential for effective management and prevention. This project was undertaken to design and implement a web-based diabetes prediction system using machine learning techniques. The study employed the Pima Indians Diabetes Dataset obtained from Kaggle, which contains 768 patient records with eight medical attributes, including glucose level, blood pressure, body mass index (BMI), age, and family history. The dataset was preprocessed to handle missing values, outliers, and feature scaling before being divided into training and testing sets. A Logistic Regression model was developed to perform the prediction task. The choice of this algorithm was based on its simplicity, efficiency, and interpretability in binary classification problems. The model was trained and evaluated using standard metrics such as accuracy, precision, recall, and F1-score, and the results confirmed its reliability in predicting diabetes outcomes. For practical implementation, the trained model was integrated into a Flask-based web application with a user-friendly HTML/CSS interface and deployed on Vercel. The system enables users to input clinical details and receives instant predictions of diabetes risk. While not intended to replace professional medical diagnosis, the application provides a cost-effective and accessible tool for early screening and awareness. This project demonstrates the potential of machine learning in healthcare and establishes a foundation for future improvements, including the use of larger datasets, and additional predictive features.
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