Early Prediction and Progrssion of Chronic Kidney Disease Using Machine Lerning Techniques

A serious medical condition is chronic kidney disease that necessitates early detection and continuing monitoring to avoid harmful consequences. This report describes a groundbreaking study of early CKD detection and progression tracking utilizing machine learning approaches applied to real-time clinical datasets. Predictive models are developed using a varied range of clinical tests and patient data to provide reliable insights into CKD development and progression. The proposed method effectively evaluates longitudinally gathered data by combining test findings with medical histories. This study improves machine learning algorithms' effectiveness for early CKD detection and progression monitoring by incorporating ensemble approaches. These approaches improve accuracy and interpretability by combining diverse clinical data sources, allowing medical practitioners to optimize patient treatment and outcomes.