A Machine Learning Approach for Early Diabetes Prediction Using Large-Scale Clinical Data

al kharaim ali

doi:10.5281/zenodo.19254094

March 27, 2026Open Access

A Machine Learning Approach for Early Diabetes Prediction Using Large-Scale Clinical Data

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Key Points

This research aims to develop a machine learning model for predicting diabetes early in individuals using clinical data.
Utilized a dataset of 100,000 patient records.
Evaluated multiple machine learning models to identify the most accurate method.
Developed a web-based application for real-time diabetes risk predictions.
Achieved an accuracy rate of 97.22% with the Gradient Boosting model.
The web application can provide immediate risk assessments for clinical use.

Implication

Machine learning predicts diabetes risk in large patient dataset, indicating potential for early intervention.

Abstract

This study presents a machine learning-based approach for early diabetes prediction using a dataset of 100,000 patient records. Multiple models were evaluated, including Gradient Boosting, which achieved an accuracy of 97.22%. A web-based application was also developed to provide real-time diabetes risk predictions for clinical use.

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Cite This Study

al kharaim ali (2026) studied this question.

synapsesocial.com/papers/69c8c277de0f0f753b39cc27 https://doi.org/10.5281/zenodo.19254094

View Full Paper

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A Machine Learning Approach for Early Diabetes Prediction Using Large-Scale Clinical Data

Key Points

Implication

Abstract

Discussion

Cite This Study

A Machine Learning Approach for Early Diabetes Prediction Using Large-Scale Clinical Data

Key Points

Implication

Abstract

Discussion

Cite This Study

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