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This is a Cross-Sectional Study study.

December 4, 2025Journal of Primary Care & Community HealthOpen Access

Exploring Explainable Machine Learning for Predicting and Interpreting Self-Reported Diabetes among Tennessee Adults: Insights from the 2023 Behavioral Risk Factor Surveillance System (BRFSS)

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Authors

MMMustapha Aliyu Muhammad JSJamilu Sani MAMohamed Mustaf Ahmed

Discussion

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Overview

Cross-sectional analysis identifies social determinants and clinical factors influencing diabetes, highlighting Gradient Boosting's predictive ability.

Key Points

The Gradient Boosting model achieved an accuracy of 82%, demonstrating superior performance in predicting diabetes risk.
Key predictors included high blood pressure, body mass index, and physical inactivity, indicating areas for intervention.
Analysis involved Python and algorithms like Logistic Regression and Random Forest, ensuring robust modeling techniques were applied.
Findings emphasize the need for explainable AI, revealing how social determinants impact diabetes outcomes.

Cite This Study

Muhammad et al. (2025) studied this question.

synapsesocial.com/papers/6930e8cdea1aef094cca383f https://doi.org/10.1177/21501319251400546

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December 4, 2025Journal of Primary Care & Community HealthOpen Access

Exploring Explainable Machine Learning for Predicting and Interpreting Self-Reported Diabetes among Tennessee Adults: Insights from the 2023 Behavioral Risk Factor Surveillance System (BRFSS)

View Full Paper

Ask AI

Bookmark

Authors

MMMustapha Aliyu Muhammad JSJamilu Sani MAMohamed Mustaf Ahmed

Discussion

Member takes

Overview

Cross-sectional analysis identifies social determinants and clinical factors influencing diabetes, highlighting Gradient Boosting's predictive ability.

Key Points

The Gradient Boosting model achieved an accuracy of 82%, demonstrating superior performance in predicting diabetes risk.
Key predictors included high blood pressure, body mass index, and physical inactivity, indicating areas for intervention.
Analysis involved Python and algorithms like Logistic Regression and Random Forest, ensuring robust modeling techniques were applied.
Findings emphasize the need for explainable AI, revealing how social determinants impact diabetes outcomes.

Cite This Study

Muhammad et al. (2025) studied this question.

synapsesocial.com/papers/6930e8cdea1aef094cca383f https://doi.org/10.1177/21501319251400546

View Full Paper

Ask AI

Bookmark