سه شنبه 6 آذر 1403
دوره 15، شماره 1 - ( زمستان- در حال انتشار 1403 )
جلد 15 شماره 1 صفحات 0-0 |
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Tartibian B, Fasihi L, Eslami R, Fasihi A. Comparison and Prediction of Hepatic Encephalopathy Complications in Liver Transplant Patients Using Random Forest Algorithm in Active and Inactive Men. PTJ 2025; 15 (1)
URL: http://ptj.uswr.ac.ir/article-1-663-fa.html
URL: http://ptj.uswr.ac.ir/article-1-663-fa.html
Comparison and Prediction of Hepatic Encephalopathy Complications in Liver Transplant Patients Using Random Forest Algorithm in Active and Inactive Men. فصلنامه فیزیک درمانی. 1403; 15 (1)
چکیده: (1056 مشاهده)
Purpose: Liver diseases are among the most common diseases in the world. For liver transplant patients, the presence of postoperative problems increases the complexity of postoperative nursing. Patients' hospitalization stay is prolonged, and the costs of hospitalization increase. Data mining has become an easy way to predict diseases in recent years. Therefore, the aim of this study was to compare and predict the complications of hepatic encephalopathy in active and inactive men after liver transplantation.
Methods: The statistical population of this study was 852 people. Among these people, 350 active men (162 healthy people and 188 people with encephalopathy symptoms) and 402 inactive men (210 healthy people and 192 people with encephalopathy symptoms) were selected as study subjects. These people underwent a liver transplant in the hospital between 2010 and 2011. Random forest algorithm and 14 features from laboratory records were used to predict encephalopathy complications after liver transplantation. MATLAB software was used for data analysis.
Results: The results showed that there was no significant difference in the prediction of encephalopathy complication by random forest algorithm between active and inactive men. Also, this study showed that the random forest algorithm using 14 features is 76.2% and 75.5% accurate for diagnosing hepatic encephalopathy after liver transplantation in active and inactive men.
Conclusion: Computer-based decision support systems can help to reduce poor healthcare decisions and the expenses associated with unneeded clinical trials in both active and inactive populations. Based on the accuracy of the random forest algorithm on the data, it appears that this system can assist clinicians in forecasting the risk of hepatic encephalopathy following transplantation with high accuracy and at a cheap cost.
Methods: The statistical population of this study was 852 people. Among these people, 350 active men (162 healthy people and 188 people with encephalopathy symptoms) and 402 inactive men (210 healthy people and 192 people with encephalopathy symptoms) were selected as study subjects. These people underwent a liver transplant in the hospital between 2010 and 2011. Random forest algorithm and 14 features from laboratory records were used to predict encephalopathy complications after liver transplantation. MATLAB software was used for data analysis.
Results: The results showed that there was no significant difference in the prediction of encephalopathy complication by random forest algorithm between active and inactive men. Also, this study showed that the random forest algorithm using 14 features is 76.2% and 75.5% accurate for diagnosing hepatic encephalopathy after liver transplantation in active and inactive men.
Conclusion: Computer-based decision support systems can help to reduce poor healthcare decisions and the expenses associated with unneeded clinical trials in both active and inactive populations. Based on the accuracy of the random forest algorithm on the data, it appears that this system can assist clinicians in forecasting the risk of hepatic encephalopathy following transplantation with high accuracy and at a cheap cost.
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