DiabetoAI: Diabetes Disease Prediction System Using Six Machine Learning Models
Description
Diabetes is a growing global health concern, affecting millions of individuals and placing immense pressure on healthcare systems. Early and accurate prediction of diabetes can significantly improve patient outcomes and reduce the burden on healthcare providers. This study explores the application of six machine learning algorithms—Logistic Regression, Decision Trees, Random Forest, Support Vector Machines (SVM), Gradient Boosting Decision Trees, and XGBoost (Extreme Gradient Boosting). to predict diabetes based on key health indicators. A comprehensive dataset is preprocessed and analyzed to ensure high-quality input data, and the models are evaluated using metrics such as accuracy, precision, recall, and F1-score. The comparative analysis of these algorithms highlights their strengths and weaknesses in diabetes prediction. This work aims to provide a scalable and efficient predictive model for aiding healthcare professionals in identifying individuals at risk of developing diabetes, contributing to better preventive and diagnostic strategies.
Files
Diabetes Disease Prediction System.pdf
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(5.9 MB)
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Additional details
Dates
- Submitted
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2025-01