Performance Comparison of Classification Algorithms for Chronic Kidney Disease Prediction
DOI:
https://doi.org/10.57152/ijatis.v1i2.1120Keywords:
Chronic Kidney Disease, Decision Tree, Ensemble Learning, Extra Trees, XGBoostAbstract
Chronic Kidney Disease (CKD) is an abnormal kidney function or failure of the kidneys to filter the bloodstream and remove metabolic waste that progresses over months or years. Chronic kidney disease is asymptomatic in its early stages. It has no age limit, and if you already suffer from chronic kidney disease, the likelihood of a sudden decline in kidney function increases. The medical record data of chronic kidney disease patients can be utilized to make predictions and can be processed using machine learning to classify the risk of death. This research will use Ensemble Learning, which combines Decision Tree, XGBoost, and Extra Trees algorithms. In the pre-processing stage, value filling is carried out using the random sampling method. It was concluded that the highest accuracy value in Extra Trees was 96%. In comparison, the Decision Tree was 94%, and the XGBoost method obtained 95% accuracy so that Pathologists can use it in developing a program to predict chronic kidney disease
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