Improving Enhanced Clinical Decision Making : Chronic Kidney Disease Detection

Chronic kidney disease (CKD) is among the health concerns that warrant attention for people worldwide and has impacts, on individuals and the economy. The “Chronic Kidney Disease Detection” project combines nephrology and machine learning to improve the classification and early detection of CKD. We start by processing and organizing healthcare records creating a dataset that includes clinical, biochemical and demographic factors. To ensure accuracy we employ techniques to clean the data and minimize errors caused by outliers. Working closely with experts in both the field and machine learning we use methods to identify important features for analysis. This involves encoding variables and normalizing continuous features resulting in a feature space that includes crucial markers of renal function. Our main goal is to develop machine learning models like networks, random forests, AdaBoost classifiers, gradient boosting classifiers and CatBoost classifiers that can uncover patterns within multidimensional data. We fine tune these models architectures and techniques to extract representations while still maintaining interpretability. Through training over iterations or “epochs” our models achieve the level of robustness needed for practical implementation while demonstrating exceptional accuracy, in detecting CKD and categorizing its different stages. This research introduces a creative method that has implications, in the medical field. This model can be improve patient outcomes for slow down disease advancement allow for intervention and tailor treatment plans, to patients.