Volume 17, Issue 6 (2-2024)
payavard 2024, 17(6): 571-582 |
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Ethics code: IR.TUMS.SPH.REC.1402.037
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Moalem Borazjani F, Yazdani A, Safdari R, Gatmiri S M. Predicting Kidney Transplantation Outcomes Using Machine Learning Techniques. payavard 2024; 17 (6) :571-582
URL: http://payavard.tums.ac.ir/article-1-7661-en.html
URL: http://payavard.tums.ac.ir/article-1-7661-en.html
1- Master of Sciences Student in Health Information Technology, School of Allied Medical Sciences, Tehran University of Medical Sciences, Tehran, Iran
2- Assistant Professor, Department of Health Information Management, Health Human Resources Research Center, Clinical Education Research Center, School of Health Management and Information Sciences, Shiraz University of Medical Sciences, Shiraz, Iran
3- Professor, Department of Health Information Management, School of Allied Medical Sciences, Tehran University of Medical Sciences, Tehran, Iran ,rsafdari@tums.ac.ir
4- Associate Professor, Department of Nephrology, Nephrology Research Center, Center of Excellence in Nephrology, School of Medicine, Imam Khomeini Hospital, Tehran University of Medical Sciences, Tehran, Iran
2- Assistant Professor, Department of Health Information Management, Health Human Resources Research Center, Clinical Education Research Center, School of Health Management and Information Sciences, Shiraz University of Medical Sciences, Shiraz, Iran
3- Professor, Department of Health Information Management, School of Allied Medical Sciences, Tehran University of Medical Sciences, Tehran, Iran ,
4- Associate Professor, Department of Nephrology, Nephrology Research Center, Center of Excellence in Nephrology, School of Medicine, Imam Khomeini Hospital, Tehran University of Medical Sciences, Tehran, Iran
Abstract: (571 Views)
Background and Aim: Kidney failure is a common and increasing problem in Iran and worldwide. Kidney transplantation is recognized as a preferred treatment method for patients with end-stage renal disease (ESRD). Machine learning, as one of the most valuable branches of artificial intelligence in the field of predicting patient outcomes or predicting various conditions in patients, has significant applications. The purpose of this research was to predict kidney transplant outcomes in patients using machine learning.
Materials and Methods: Since CRISP is one of the strongest methodologies for implementing data mining projects, it was chosen as the working method. In order to identify the factors affecting the prediction of kidney transplant outcomes, a researcher-created checklist was sent to some of nephrologists nationwide to determine the importance of each factor. The results were analyzed and examined. Then, using Python language and different algorithms such as random forest, SVM, KNN, deep learning, and XGBoost the data was modeled.
Results: The final model was multilabel, capable of predicting various kidney transplant outcomes, including rejection probability, diabetic reactions, malignant reactions, and patient rehospitalization. After modeling the input data features, the model was able to predict the four kidney transplant outcomes such as rejection, diabetes, malignancy and readmission with an error rate of less than 0.01.
Conclusion: The high level of accuracy and precision of the random forest model demonstrates its strong predictive power for forecasting kidney transplant outcomes. In this study, the most influential factors contributing to patient susceptibility to the mentioned outcomes were identified. Using this machine learning-based system, it is possible to predict the probability of these outcomes occurring for new cases.
Materials and Methods: Since CRISP is one of the strongest methodologies for implementing data mining projects, it was chosen as the working method. In order to identify the factors affecting the prediction of kidney transplant outcomes, a researcher-created checklist was sent to some of nephrologists nationwide to determine the importance of each factor. The results were analyzed and examined. Then, using Python language and different algorithms such as random forest, SVM, KNN, deep learning, and XGBoost the data was modeled.
Results: The final model was multilabel, capable of predicting various kidney transplant outcomes, including rejection probability, diabetic reactions, malignant reactions, and patient rehospitalization. After modeling the input data features, the model was able to predict the four kidney transplant outcomes such as rejection, diabetes, malignancy and readmission with an error rate of less than 0.01.
Conclusion: The high level of accuracy and precision of the random forest model demonstrates its strong predictive power for forecasting kidney transplant outcomes. In this study, the most influential factors contributing to patient susceptibility to the mentioned outcomes were identified. Using this machine learning-based system, it is possible to predict the probability of these outcomes occurring for new cases.
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