Papers › Performance Analysis of Machine Learning Algorithms in Chronic Kidney Disease Prediction
Performance Analysis of Machine Learning Algorithms in Chronic Kidney Disease Prediction
Iftekhar Ahmed, Tanzil Ebad Chowdhury, Biggo Bushon Routh, Nafisa Tasmiya, Shadman Sakib, Adil Ahmed Chowdhury
Kidneys are the filter of the human body. About 10 % of the global population is thought to be affected by Chronic Kidney Disease (CKD), which causes kidney function to decline. To protect in danger patients from additional kidney damage, effective risk evaluation of CKD and appropriate CKD monitoring are crucial. Due to quick and precise detection capabilities, Machine Learning models can help practitioners accomplish this goal efficiently therefore an enormous number of diagnosis systems and processes in the healthcare sector nowadays are relying on machine learning due to its disease prediction capability. In this study, we designed and suggested disease predictive computer-aided designs for the diagnosis of CKD. The dataset for CKD is attained from the repository of machine learning of UCL, with a few missing values; those are filled in using “mean/mode” and “Random sampling method” strategies. After successfully achieving the missing data, eight ML techniques (Random Forest, SVM, Naive Bayes, Logistic Regression, KNN, XG _Boost, Decision Tree and AdaBoost) were used to establish models and the performance evaluation comparisons among the result accuracies are measured by the techniques to find the machine learning models with highest accuracies. Among them, Random Forest as well as Logistic Regression showed an outstanding 99% accuracy, followed by the Ada Boost, XG-Boost, Naive Bayes, Decision Tree, and SVM whereas the KNN classifier model stands last with an accuracy of 73%.
Code
Repository list and official/mentioned flags are the archive's, frozen 2025-07-28. Reachability, where shown, is from one Syntology probe window (2026-09-16 to 2026-09-18); repositories not probed show nothing. GitHub stars are not tracked.
Code Syntology ran Syntology
Not run by Syntology. Nothing on this page verifies that the listed code works.
Tasks
Results from the paper archive 2025-07-28
No leaderboard rows for this paper in the archive.
Report a problem or propose a change · a person checks every report against the paper or source before anything changes; decisions are listed on /corrections