Comparative Analysis on Prediction of Chronic KidneyDisease by various Machine Learning Models

Abstract

Chronic Kidney Disease (CKD) is a prevalent global healthconcern, which is affecting a number of people in the whole world. Earlydetection & precise checking of CKD are really important for essentialsupervision & intervention to prevent disease progression and associatedcomplications. Machine learning (ML) models have emerged as favourabletools for CKD detection due to their ability to analyse complex data patternsand provide predictive insights. This abstract explores the application ofvarious ML techniques in CKD detection, encompassing diverse datasources such as demographic information, clinical biomarkers, medicalimaging, and genetic data. We review the performance of different MLalgorithms including SVM, Decision Tree, Random Forest, NeuralNetworks, & ensemble methods in CKD prediction tasks. In recent years,machine learning techniques have garnered considerable attention for theirefficacy in medical diagnosis and prognosis, with Random Forest (RF)emerging as a prominent algorithm due to its robustness and versatility. Thisabstract investigates the application of Random Forest in CKD detection,utilising diverse datasets encompassing demographic information, clinicalbiomarkers, medical imaging, and genetic profiles. We explore the processof feature selection, model training, and evaluation within the RFframework, highlighting the ability to manage high-dimensional data andnonlinear relationships effectively. Moreover, we review studies showcasingthe performance of RF models in predicting CKD onset, progression, andrisk stratification, comparing its efficacy with other machine learningapproaches. Challenges such as data imbalance, interpretability, and modelcalibration are also discussed, along with potential strategies to addressthese issues & enhance the clinical utility of RF-based CKD detectionsystems. Ultimately, this abstract underscores the promising role of RandomForest as a valuable method in detection and management of ChronicKidney Disease, offering insights that can potentially improve patientoutcomes and healthcare delivery.