Data to Diagnosis: Evaluating Machine Learning Algorithms for Predictive Healthcare in Diabetes

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This research shows machine learning algorithms improve diabetes diagnosis, indicating effective tools for early intervention.

Ensemble models like Gradient Boosting reported over 91% accuracy, highlighting their potential in diabetes prediction.
The study utilized eight machine learning algorithms, evaluating their performance using accuracy, precision, and recall.
Despite an imbalanced dataset with ~10% diabetic cases, findings support the use of machine learning in early diagnosis.
Normalization and categorical encoding were key preprocessing steps in developing the machine learning models.

Talpur et al. (2025) studied this question.

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View Full Paper

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Authors

This research shows machine learning algorithms improve diabetes diagnosis, indicating effective tools for early intervention.

Ensemble models like Gradient Boosting reported over 91% accuracy, highlighting their potential in diabetes prediction.
The study utilized eight machine learning algorithms, evaluating their performance using accuracy, precision, and recall.
Despite an imbalanced dataset with ~10% diabetic cases, findings support the use of machine learning in early diagnosis.
Normalization and categorical encoding were key preprocessing steps in developing the machine learning models.

Talpur et al. (2025) studied this question.

View Full Paper

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