| REVIEW ARTICLE |
|
| Year : 2023 | Volume
: 14
| Issue : 1 | Page : 110 |
|
|
Using data mining techniques to predict chronic kidney disease: A review study
Mohammad Sattari1, Maryam Mohammadi2
1 Health Information Technology Research Center, Isfahan University of Medical Sciences, Isfahan, Iran
2 Department of Management and Health Information Technology, School of Management and Medical Information Sciences, Isfahan University of Medical Sciences, Isfahan, Iran
Correspondence Address:
Maryam Mohammadi
Department of Management and Health Information Technology, School of Management and Medical Information Sciences, Isfahan University of Medical Sciences, Isfahan
Iran
 Source of Support: None, Conflict of Interest: None  | Check |
DOI: 10.4103/ijpvm.ijpvm_482_21
|
|
|
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.
|
|
|
|
| [FULL TEXT] [PDF]* |
|
 |
|
Next article
