Early Detection of Chronic Kidney Disease: The Data Mining AI Fusion

chronic kidney disease or CKD is a major universal medical issue, which impacts infinite number of individuals worldwide. Early diagnosis and treatment of CKD are essential for stopping disease progression and lowering the risk of consequences. Data mining (DM) methods have been applied recently to find people who might be at risk of getting CKD. In order to identify early-stage CKD using electronic health records, this study suggests hybrid approaches that include various data mining approaches, such as association rule mining, decision trees (DT), and logistic regression (LR). The outcomes show the potential of data mining and machine learning algorithms to enhance early CKD detection and offer prompt therapies to slow or reverse the disease's progression. The proposed hybrid method can be utilized as an additional tool to help healthcare professionals find patients who are at risk and enhance patient outcomes. The efficacy of this strategy in various patient populations and healthcare settings needs to be further investigated.