Using Data Mining Techniques to Predict Chronic Kidney Disease: A Review Study Publisher

Summary: Which data tools help predict kidney disease? A review found random forests often performed well, while albumin, age and creatinine were frequently used predictors. #KidneyHealth #MachineLearning

Sattari M ; Mohammadi M
Source: International Journal of Preventive Medicine Published:2023

Abstract

One of the growing global health problems is chronic kidney disease (CKD). Early diagnosis, control, and management of chronic kidney disease are very important. This study considers articles published in English between 2016 and 2021 that use classification methods to predict kidney disease. Data mining models play a vital role in predicting disease. Through our study, data mining techniques of support vector machine, Naive Bayes, and k‑nearest neighbor had the highest frequency. After that, random forest, neural network, and decision tree were the most common data mining techniques. Among the risk factors associated with chronic kidney disease, respectively, risk factors of albumin, age, red blood cells, pus cells, and serum creatinine had the highest frequency in these studies. The highest number of best yields was allocated to random forest technique. Reviewing larger databases in the field of kidney disease can help to better analyze the disease and ensure the risk factors extracted. © 2023 International Journal of Preventive Medicine.