Optimizing Chronic Kidney Disease Prediction Through Advanced Data Imputation and Clustering

Chronic Kidney Disease (CKD) is a slowly worsening disease that can hurt the patients in large numbers from all over the world. The article employs a mixed machine learning technique that unites clustering and supervised learning in order to detect CKD. The data has several checks applied, namely, typographical errors fixed, missing value estimation will be done using K-Nearest Neighbors(KNN) and feature optimization. The unsupervised techniques such as clustering will be adopted with that of the prediction of the hidden patterns that are not directly observable which are the most difficult models for a human to come up with. Moreover, the predictive models are re-trained on the enhanced dataset in order to offer a highly efficient CKD classification test. The study has shown that the clustering analysis makes it possible for the interpretation of high-risk cohorts to be conducted while giving inputs in identifying the progression of the disease. The phase of predictive modeling involves the use of different machine learning algorithms to evaluate the functionalities to ensure the correct classification. The merger of clustering with predictive analytics offers a new way for finding the CKD diagnosis and early detection strategies. The research points out the importance of data-driven methods in health care and shows how a preprocessing pipeline is built up with advanced technologies that could generate almost exact predictions. They also lead to the capability of AI-based processes where these tools can assist medical personnel in the diagnosis of early CKD and intervention. The pathway that future scientific research might follow refers to the specific niche of deep learning and the wider use of datasets which would both bring up the advantage of generalizability.