An Interpretable Machine Learning Technique for Chronic Kidney Disease Diagnosis Using Clinical Data
The research presents a machine learning framework for early CKD diagnosis using structured clinical data from the UCI Machine Learning Repository. Multiple algorithms—logistic regression, SVM, random forest, XGBoost, LightGBM, and CatBoost—were evaluated within a single preprocessing pipeline, achieving F1-scores between 0.98 and 0.99, with SVM attaining the highest mean F1 of 0.9870. Validation through label permutation confirmed that the models learned meaningful clinical correlations rather than random noise.
The research presents a machine learning framework for early CKD diagnosis using structured clinical data from the UCI Machine Learning Repository. Multiple algorithms—logistic regression, SVM, random forest, XGBoost, LightGBM, and CatBoost—were evaluated within a single preprocessing pipeline, achieving F1-scores between 0.98 and 0.99, with SVM attaining the highest mean F1 of 0.9870. Validation through label permutation confirmed that the models learned meaningful clinical correlations rather than random noise.
The high accuracy and interpretability of the models support Swiss AI initiatives aimed at reliable, data-driven medical diagnostics in healthcare.
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