Chronic Kidney Disease (CKD) affects 697.5 million people globally, with \~{}115 million in India. Early detection is clinically challenging as the disease remains asymptomatic through stages 1–3, particularly in resource-limited rural settings like Madhya Pradesh. While high-accuracy black-box models (SVM, Random Forest, DNN) achieve 91.5–98% accuracy on the UCI CKD benchmark, they lack interpretability—creating a Transparency Gap that hinders clinical adoption. This paper proposes ANFIS-PSO, a Particle Swarm Optimization-tuned Adaptive Neuro-Fuzzy Inference System for early CKD diagnosis. A five-stage pipeline incorporating KNN imputation and Min-Max normalization was applied to the... More >