Empowering Preventive Healthcare: Machine Learning-Based Diabetes Risk Screening Using Survey Data

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This analysis demonstrates high-risk diabetes identification in underserved populations, suggesting effective preventive healthcare strategies.

The Voting Classifier model achieved the highest recall rate of 0.62, indicating effective risk detection.
Machine learning models were tested using the BRFSS dataset, comprising over 300,000 health survey data records.
Optimization techniques, including Recursive Feature Elimination and class balancing, enhanced model performance.
The work aligns with Sustainable Development Goals, addressing healthcare inequities in community settings.

Sulaiman et al. (2025) studied this question.

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View Full Paper

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Authors

This analysis demonstrates high-risk diabetes identification in underserved populations, suggesting effective preventive healthcare strategies.

The Voting Classifier model achieved the highest recall rate of 0.62, indicating effective risk detection.
Machine learning models were tested using the BRFSS dataset, comprising over 300,000 health survey data records.
Optimization techniques, including Recursive Feature Elimination and class balancing, enhanced model performance.
The work aligns with Sustainable Development Goals, addressing healthcare inequities in community settings.

Sulaiman et al. (2025) studied this question.

View Full Paper

Ask AI

Bookmark