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Dergiler / Balkan Journal of Electrical and Computer Engineering / 2026 / Cilt: 13 Sayı: 2

Leveraging SHAP for Interpretable Diabetes Prediction: A Study of Machine Learning Models on the Pima Indians Diabetes Dataset

İsmail Kırbaş(BURDUR MEHMET AKİF ERSOY ÜNİVERSİTESİ)0000-0002-1206-8294
Ahmet Çifci(BURDUR MEHMET AKİF ERSOY ÜNİVERSİTESİ, MÜHENDİSLİK-MİMARLIK FAKÜLTESİ)0000-0001-7679-9945
Sayfa
128–139
Yayın Tarihi
2025-06-30
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⚡ TL;DR(1 Cümlede Hızlı Çıkarım)

The study emphasizes the necessity for interpretable ML in healthcare, proposing SHAP as a valuable tool for bridging predictive accuracy and clinical transparency in diabetes diagnostics.

📌 3 Maddede Bu Makale

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Amaç & Hipotez:This paper investigates the application of machine learning (ML) models for predicting diabetes using the Pima Indians Diabetes Database, with a focus on enhancing model interpretability through the use of SHapley Additive exPlanations (SHAP).
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Temel Bulgular & Veri:The study evaluates eight ML models, including Adaptive Boosting (AdaBoost), k-Nearest Neighbors (k-NN), Logistic Regression (LR), Multi-layer Perceptron (MLP), Naive Bayes (NB), Random Forest (RF), Support Vector Machine (SVM), and eXtreme Gradient Boosting (XGBoost), utilizing both test/train split and 10-fold cross-validation methods.
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Sonuç & Katkı:The study emphasizes the necessity for interpretable ML in healthcare, proposing SHAP as a valuable tool for bridging predictive accuracy and clinical transparency in diabetes diagnostics.
🔬Tür:Modelleme & Sayısal Simülasyon
📍Kapsam:Genişletilmiş Akademik Örneklem
⚙️Değişkenler:Diabetes Prediction · Explainable Artificial Intelligence · Machine Learning Models

Abstract

This paper investigates the application of machine learning (ML) models for predicting diabetes using the Pima Indians Diabetes Database, with a focus on enhancing model interpretability through the use of SHapley Additive exPlanations (SHAP). The study evaluates eight ML models, including Adaptive Boosting (AdaBoost), k-Nearest Neighbors (k-NN), Logistic Regression (LR), Multi-layer Perceptron (MLP), Naive Bayes (NB), Random Forest (RF), Support Vector Machine (SVM), and eXtreme Gradient Boosting (XGBoost), utilizing both test/train split and 10-fold cross-validation methods. The RF model demonstrated superior performance, achieving an accuracy of 82% and an F1-score of 0.83 in the test/train split, and an accuracy of 83% and an F1-score of 0.84 in the 10-fold cross-validation. SHAP analysis was employed to identify the most influential predictors, revealing that glucose, BMI, pregnancies, and insulin levels are the key factors in diabetes prediction, aligning with established clinical markers. Additionally, the use of the Synthetic Minority Over-sampling TEchnique (SMOTE) for class balancing and data scaling contributes to robust model performance. The study emphasizes the necessity for interpretable ML in healthcare, proposing SHAP as a valuable tool for bridging predictive accuracy and clinical transparency in diabetes diagnostics.

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Bu Makaleye Atıf Yap

Kırbaş, İ., Çifci, A. (2025). Leveraging SHAP for Interpretable Diabetes Prediction: A Study of Machine Learning Models on the Pima Indians Diabetes Dataset. Balkan Journal of Electrical and Computer Engineering, Cilt: 13 Sayı: 2, 128–139. https://doi.org/10.17694/bajece.1577929

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