Diabetes Disease Prediction Using Machine Learning Classification Algorithms

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Machine learning algorithms enhance accuracy in diabetes prediction models, suggesting efficiency in healthcare applications.

Logistic Regression achieved the highest accuracy of 97.53%, indicating strong predictive power for diabetes screening.
Nine different machine learning algorithms were assessed, utilizing 5-fold cross-validation for evaluating model performance.
Feature selection techniques like Random Forest improved efficiency while maintaining accuracy in the diabetes prediction model.
The findings support the use of machine learning in developing scalable, real-time systems for diabetes screening in healthcare.

Pamutha et al. (2025) studied this question.

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View Full Paper

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Authors

Machine learning algorithms enhance accuracy in diabetes prediction models, suggesting efficiency in healthcare applications.

Logistic Regression achieved the highest accuracy of 97.53%, indicating strong predictive power for diabetes screening.
Nine different machine learning algorithms were assessed, utilizing 5-fold cross-validation for evaluating model performance.
Feature selection techniques like Random Forest improved efficiency while maintaining accuracy in the diabetes prediction model.
The findings support the use of machine learning in developing scalable, real-time systems for diabetes screening in healthcare.

Pamutha et al. (2025) studied this question.

View Full Paper

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