Early Detection of Chronic Kidney Disease Using a Machine learning Approach for Better Results

Chronic Kidney Disease is a progressive disorder that requires reliable diagnostic methods for effective clinical management. In this paper, a machine-learning–based system developed for detecting CKD using clinical and laboratory data is presented. Based on the analytics performed on the key medical parameters like blood pressure, specific gravity, albumin, serum creatinine, and other biomarkers, it classifies whether the patient is likely to be suffering from CKD or not. The dataset is preprocessed by handling missing values, encoding categorical features, and normalizing numerical attributes to make the data consistent for better model performance. Feature selection techniques will reduce model complexity and improve the prediction quality of the model. A Random Forest classifier was implemented and tested for its ability in the effective detection of CKD. Model performance will be measured primarily on accuracy because it will easily tell how well CKD and non-CKD are stratified. Further refinement of the model is done by the technique of hyperparameter tuning in order to improve its predictive sthe model’s decision-making process, and the results will be more understandable clinically. This work has shown that machine learning can support healthcare professionals with a reliable, data-driven approach for CKD detection that contributes to improved diagnostic assistance and the management of patients.