A machine learning framework for predicting long-term graft survival after kidney transplantation

Badrouchi, S.; Ahmed, Abdulaziz; Bacha, Mohamed Mongi; Abderrahim, E.; Abdallah, T. Ben

doi:10.1016/j.eswa.2021.115235

Cited by 15 publications

(7 citation statements)

References 68 publications

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“…Previous studies have established several effective prediction data-driven models to identify kidney post-transplantation graft survival rates [ 9 , 10 , 17 ]. Our previous study dealt with data and modeling to predict post-transplantation graft survival among kidney transplant recipients using data mining algorithms [ 18 ].…”

Section: Introductionmentioning

confidence: 99%

Predicting the survival of kidney transplantation: design and evaluation of a smartphone-based application

et al. 2022

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Background Prediction of graft survival for Kidney Transplantation (KT) is considered a risky task due to the scarcity of donating organs and the use of health care resources. The present study aimed to design and evaluate a smartphone-based application to predict the survival of KT in patients with End-Stage Renal Disease (ESRD). Method Based on the initial review, a researcher-made questionnaire was developed to assess the information needs of the application through urologists and nephrologists. By using information obtained from the questionnaire, a checklist was prepared, and the information of 513 patients with kidney failure was collected from their records at Sina Urological Research Center. Then, three data mining algorithms were applied to them. The smartphone-based application for the prediction of kidney transplant survival was designed, and a standard usability assessment questionnaire was used to evaluate the designed application. Results Three information elements related to the required data in different sections of demographic information, sixteen information elements related to patient clinical information, and four critical capabilities were determined for the design of the smartphone-based application. C5.0 algorithm with the highest accuracy (87.21%) was modeled as the application inference engine. The application was developed based on the PhoneGap framework. According to the participants’ scores (urologists and nephrologists) regarding the usability evaluation of the application, it can be concluded that both groups participating in the study could use the program, and they rated the application at a "good" level. Conclusion Since the overall performance or usability of the smartphone-based app was evaluated at a reasonable level, it can be used with certainty to predict kidney transplant survival.

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Section: Introductionmentioning

confidence: 99%

Predicting the survival of kidney transplantation: design and evaluation of a smartphone-based application

et al. 2022

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“…In order to confirm the reliability of our model we also performed cross-validation. These results are promising and the ML framework we used 10 may give better results with larger databases.…”

Section: Discussionmentioning

confidence: 83%

“…The current study builds upon prior research by our team 10 . In that work, we laid the foundation for the ML techniques employed.…”

Section: Methodsmentioning

confidence: 98%

Predicting long-term outcomes of kidney transplantation in the era of artificial intelligence

Badrouchi,

Bacha,

Ahmed

et al. 2023

Sci Rep

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The ability to accurately predict long-term kidney transplant survival can assist nephrologists in making therapeutic decisions. However, predicting kidney transplantation (KT) outcomes is challenging due to the complexity of the factors involved. Artificial intelligence (AI) has become an increasingly important tool in the prediction of medical outcomes. Our goal was to utilize both conventional and AI-based methods to predict long-term kidney transplant survival. Our study included 407 KTs divided into two groups (group A: with a graft lifespan greater than 5 years and group B: with poor graft survival). We first performed a traditional statistical analysis and then developed predictive models using machine learning (ML) techniques. Donors in group A were significantly younger. The use of Mycophenolate Mofetil (MMF) was the only immunosuppressive drug that was significantly associated with improved graft survival. The average estimated glomerular filtration rate (eGFR) in the 3rd month post-KT was significantly higher in group A. The number of hospital readmissions during the 1st year post-KT was a predictor of graft survival. In terms of early post-transplant complications, delayed graft function (DGF), acute kidney injury (AKI), and acute rejection (AR) were significantly associated with poor graft survival. Among the 35 AI models developed, the best model had an AUC of 89.7% (Se: 91.9%; Sp: 87.5%). It was based on ten variables selected by an ML algorithm, with the most important being hypertension and a history of red-blood-cell transfusion. The use of AI provided us with a robust model enabling fast and precise prediction of 5-year graft survival using early and easily collectible variables. Our model can be used as a decision-support tool to early detect graft status.

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“…The Kaggle database was used to collect the data [ 17 ] and this investigation was carried out. The procedure explains how data are produced.…”

Section: Methodsmentioning

confidence: 99%

Prevalence of Factors Related to Depressive Symptoms Among Married Individuals

Krishnan,

Batra,

Batra

et al. 2023

Cureus

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Background There are multiple studies that indicate that the psychological well-being of a couple and their life satisfaction depend on the family and society. Various factors such as family, family values, marriage style, married life, and education have a great impact on people's lives both directly and indirectly. It is important to understand the effects of these factors on married individuals' lives that lead to depression so that appropriate measures can be taken for its prevention. Objectives This research aims to find the relationship of depressive symptoms among married individuals with various factors such as their marriage style, education, and having children. Materials and methods The study included 433 married individuals from Istanbul who met the criteria for depression. The early identification and prediction of depression in married individuals have been demonstrated to benefit significantly from machine learning techniques. In this study, we used decision tree (DT) and random forest (RF) predictive modeling techniques to create a model to predict the occurrence of depression among married individuals. Results The accuracy of the DT approach was found to be 80%, and the RF approach was 60%. Our results showed that as compared to conventional statistical methods, machine learning models performed better for classifying couples. Conclusion Future support systems that employ a range of data sources to identify individuals who are extremely susceptible to developing depression among married people may be developed using these effective models.

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A machine learning framework for predicting long-term graft survival after kidney transplantation

Cited by 15 publications

References 68 publications

Predicting the survival of kidney transplantation: design and evaluation of a smartphone-based application

Predicting the survival of kidney transplantation: design and evaluation of a smartphone-based application

Predicting long-term outcomes of kidney transplantation in the era of artificial intelligence

Prevalence of Factors Related to Depressive Symptoms Among Married Individuals

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