  As the global population ages, the incidence of Chronic Kidney Disease (CKD)
is rising. CKD often remains asymptomatic until advanced stages, which
significantly burdens both the healthcare system and patient quality of life.
This research developed an explainable machine learning system for predicting
CKD in patients with cardiovascular risks, utilizing medical history and
laboratory data. The Random Forest model achieved the highest sensitivity of
88.2%. The study introduces a comprehensive explainability framework that
extends beyond traditional feature importance methods, incorporating global and
local interpretations, bias inspection, biomedical relevance, and safety
assessments. Key predictive features identified in global interpretation were
the use of diabetic and ACEI/ARB medications, and initial eGFR values. Local
interpretation provided model insights through counterfactual explanations,
which aligned with other system parts. After conducting a bias inspection, it
was found that the initial eGFR values and CKD predictions exhibited some bias,
but no significant gender bias was identified. The model's logic, extracted by
scoped rules, was confirmed to align with existing medical literature. The
safety assessment tested potentially dangerous cases and confirmed that the
model behaved safely. This system enhances the explainability, reliability, and
accountability of the model, promoting its potential integration into
healthcare settings and compliance with upcoming regulatory standards, and
showing promise for broader applications in healthcare machine learning.
