DESIGN AND IMPLEMENTATION OF MACHINE LEARNING BASED EARLY PROGNOSTICATION OF CHRONIC KIDNEY DISEASE

– End stage renal disease (ESRD) describes the most severe stage of chronic kidney disease (CKD), when patients need dialysis or renal transplant. There is often a delay in recognizing, diagnosing, and treating the various stages of CKD. The objective of the present study was to employ machine learning algorithms to develop a prediction model for progression to ESRD based on a large-scale multidimensional database. Machine learning (ML) techniques are excellent in predicting CKD. The current study offers a methodology for predicting CKD status using clinical data, which incorporates data preprocessing, data balancing and model design. A technique for managing missing values is done with the help of data aggregation and feature extraction. A number of physiological variables, as well as ML techniques such as logistic regression (LR), decision tree (DT) classification, and k-nearest neighbor (KNN), were used in this work to train three distinct models for reliable prediction. Compared to prior research, the accuracy rate of the models employed in this study is considerably greater, implying that they are more trustworthy than the models used in previous studies as well. A large number of model comparisons have shown their resilience, and the scheme may be inferred from the study’s result