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Diabetes Risk Prediction Using Machine Learning and Explainable AI
Stephen Mwaura · Angela Masaki · Diana Byegon · Kevin Kisengu · Orandi Felix
Moringa School · Nairobi · 2026
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Download PDFMethodology Pipeline
01
Data Understanding
BRFSS survey dataset — 14 clinical features, binary diabetes outcome.
02
EDA
Class distribution, feature correlations, outlier analysis.
03
Feature Engineering
Median imputation, StandardScaler, ordinal encoding.
04
Model Training
Six classifiers compared: LR, DT, RF, XGBoost, LightGBM, CatBoost.
05
Evaluation
Accuracy, F1, ROC-AUC, confusion matrix, 5-fold cross-validation.
06
Explainability
SHAP global importance + LIME patient-level explanations.