Enhancing Chronic Kidney Disease Prediction through Comparative Analysis of Decision Tree and Random Forest Algorithms

A major public health concern, chronic kidney disease (CKD) is marked by a high incidence and serious consequences of a progressive loss of kidney function over time. The basis of effective treatment and care is a precise early diagnosis. This study is justified by the growing prevalence of CKD and the need for accurate predictive models for early detection. Application of Decision Tree (DT) and Random Forest (RF) algorithms to CKD prediction is investigated in this work. DT, a simple yet powerful classification technique, creates a model that predicts the value of a target variable based on several input variables. RF, an ensemble learning method, constructs multiple decision trees and merges their outcomes to improve prediction accuracy and control overfitting. Furthermore, the dataset includes patient medical records with demographic, clinical, and laboratory data such as RBC’s, WBC’s, etc. in the study. Moreover, the methodology consists of preprocessing of the data, feature selection, and Decision tree and Random Forest algorithm implementation. Furthermore, results show how well both models function; RF shows better accuracy of 93% whereas DT shows 79% respectively. The work shows, in summary, that machine learning methods—RF in particular—can greatly raise the accuracy of CKD prediction. Such models might be included into healthcare systems to help with early detection and improved control of chronic kidney disease, which would eventually lead to better patient outcomes. These models will be improved and their application in various patient populations will be investigated in future studies.