Chronic Kidney Disease Early Detection using Machine Learning: Review and Critical Insights

Chronic Kidney Disease (CKD) is a significant global health concern reported to be affecting 14% of the world population, which is a serious burden to the country since it has no symptoms, and the worsening of kidney functioning occurs faster over time. The delay or prevention of development to end-stage renal disease depends on early detection and accuracy, which improves the survival and lives of patients. This review critically assesses the recent use of machine learning in the early diagnosis of CKD on the basis of their diagnostic performance, consistency, and whether they can be placed in clinical practice. The discussion of studies that have utilized algorithms such as Random Forest, XGBoost, Support Vector Machines, Convolutional Neural Networks(CNN), and hybrid deep learning models demonstrates the superiority of these algorithms over their conventional diagnostic counterparts. Conclusions of the reviewed literature demonstrate that the principles of a machine learning approach make a better result in terms of classification accuracy and predictive certainty than traditional methods can offer. When feature selection is optimized, Random Forest models achieved 100% accuracy, XGBoost showed 94-95%, hybrid CNN-SVM showed 96.18% and advanced GRU-BiLSTM architectures showed 96.5%. Incorporation of explainable AI techniques such as SHAP analysis and an interpretable hybrid framework added to the clinical trust factor by having clear and explainable information with respect to model predictions. The results highlight the paradigm-shift possibilities of machine learning to enhance the diagnosis of CKD and mainstream it into daily clinical routine.