Predictive Analysis of Chronic Kidney Disease in Machine Learning

Chronic kidney disease is a systemic disease of multiple factors and slow progression, and is now becoming a rapidly changing global pathological problem affecting healthcare systems. Anyone who can go through diagnosis before getting to stage 5 Chronic Kidney Disease (CKD) or end stage renal failure has a better shot at the result. This work involves 1659 patient records and dependent variables include demographics, lifestyle, and clinical biochemistry of CKD. Based on the supervised techniques of machine learning which are Random Forest, K Nearest Neighbors (KNN), Logistic Regression, and Naïve Bayes, it was agreed that the performance of the model metrics such as accuracy, precision and recall would need to be used. These models were applied, evaluated by means of more or less simple effectiveness parameters including, for instance, accuracy, precision, or recall. Out of these [best algorithm] achieved [accuracy value] % of predictive accuracy in CKD, and so can be used for diagnosis of CKD in its early stage. This work offers the Framework and results in the development of data-integrated approaches in healthcare and improves the disease control and management.