Volume 81, Issue 4 (July 2023)
Tehran Univ Med J 2023, 81(4): 307-318 |
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Jamshidi M, Jamshidi V. Realizing the early prediction chronic kidney disease based-on identifing. Tehran Univ Med J 2023; 81 (4) :307-318
URL: http://tumj.tums.ac.ir/article-1-12508-en.html
URL: http://tumj.tums.ac.ir/article-1-12508-en.html
1- Department of Internal Medicine, Faculty of Medicine, Rafsanjan University of Medical Sciences, Rafsanjan, Iran.
2- Department of Computer Engineering, Faculty of Engineering, Shahid Bahonar University of Kerman, Kerman, Iran.
2- Department of Computer Engineering, Faculty of Engineering, Shahid Bahonar University of Kerman, Kerman, Iran.
Abstract: (611 Views)
Background: Due to the fact that various factors are involved in the development of chronic kidney disease, this disease appears with different clinical and laboratory symptoms. The variety in type and number of clinical symptoms often misguides the treating physician. The aim of this study is to extract the key features of the disease and find the best data mining methods to improve the accuracy of kidney disease diagnosis.
Methods: This cross-sectional study was conducted from September 2021 to March 2023 for 30 months at Rafsanjan Ali Ebn Abi Taleb Hospital. Predictive models were developed and tested using different combinations of disease characteristics and seven data mining methods in RapidMiner Studio software. The limitations of the study are as follows: 1) The models were based on 40-year-old and older patients records, which may limit the generalization of results to a wider age group. 2) Despite the high accuracy and comprehensiveness of the method, the models were based only on the information of kidney disease patients at Ali Ibn Abi Talib Rafsanjan Hospital. 3) The climate parameter has not been considered in the data set of the investigation to discover the hidden relationships of this parameter with the kidney disease.
Methods: This cross-sectional study was conducted from September 2021 to March 2023 for 30 months at Rafsanjan Ali Ebn Abi Taleb Hospital. Predictive models were developed and tested using different combinations of disease characteristics and seven data mining methods in RapidMiner Studio software. The limitations of the study are as follows: 1) The models were based on 40-year-old and older patients records, which may limit the generalization of results to a wider age group. 2) Despite the high accuracy and comprehensiveness of the method, the models were based only on the information of kidney disease patients at Ali Ibn Abi Talib Rafsanjan Hospital. 3) The climate parameter has not been considered in the data set of the investigation to discover the hidden relationships of this parameter with the kidney disease.
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Results: The results of the experiments in this study showed that the proposed prediction model using the Bayes method and eight identified key features (age, renal biopsy, uremia, sedimentation, albumin, edema, nocturnal enuresis, and urine-specific gravity), can detect kidney disease in people of different clinical characteristics, with 99.38% accuracy.
Conclusion: Considering that the early diagnosis of kidney disease and the adoption of appropriate treatment methods can prevent the progression of kidney damage, in this study, an attempt has been made to achieve this goal by using new statistical methods and artificial intelligence techniques. Based on the proposed method and the conducted experiments, the most important features and the best data mining method were obtained, and finally, kidney disease prediction was possible with high accuracy. |
Type of Study: Original Article |
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