Miss Fariba Moalem Borazjani, Azita Yazdani, Reza Safdari, Seyed Mansoor Gatmiri,
Volume 17, Issue 6 (2-2024)
Abstract
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.
Marzieh Latifi, Elahe Pourhossein, Amirhesam Alirezaei, Tannaz Hajialireza Tehrani, Maryam Pourhossein, Sanaz Dehghani,
Volume 19, Issue 5 (12-2025)
Abstract
Background and Aim: Sleep disorders are strongly associated with physical, mental, social health, as well as cognitive functioning. This study aimed to compare the quality of sleep between individuals on kidney transplant waiting list and kidney transplant recipients to develop an appropriate program to improve their health and quality of life.
Materials and Methods: This cross sectional descriptive-analytical study was conducted on 196 patients, including 100 patients who registered on the kidney transplant waiting list and 96 kidney transplant recipients at the Sina Hospital, Tehran University of Medical Sciences (TUMS). Convenience sampling was used. Patients completed a standardized Pittsburgh Sleep Quality Index (PSQI) questionnaire to assess sleep quality. The self-reporting method was used to complete the questionnaires. Clinical and demographic data were collected from patients’ medical files of Sina Hospital by kidney transplant coordinators. Statistical analysis was performed using SPSS, with a significance level set at less than 0.05.
Results: The mean age of the participants was 47 years, with an age range between 18 and 69 years. Sixty-eight-point Thirty-six percent of the patients were male. Based on results, no significant difference was found between patients in kidney waiting list to kidney transplanted patients in demographic variables (age, gender, marital status, number of children, job, level of education, cause of kidney disease). According to independent T- test, the mean score of sleep quality of patients on the waiting list and kidney transplant recipients was (7.75±3.55) and (4.54±3.57), respectively, indicting the significant differences between two groups (P<0.001). Also, the Pearson correlation test reveals a significant positive correlation between age and sleep quality (P=0.038, r=0.612), and a significant negative correlation between duration of dialysis and the average sleep score (P=0.040, r=-0.062).
Conclusion: It is essential to emphasis attention to the quality of sleep in kidney patients, especially during the pre-transplant and dialysis era.
Additionally, kidney transplantation can be considered an effective solution for improving sleep quality and reducing complications related to kidney failure, although some patients continue to experience sleep problems after the transplant.
