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Predicting Liver Transplantation Outcomes Through Data Analytics

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Data Science: From Research to Application (CiDaS 2019)

Part of the book series: Lecture Notes on Data Engineering and Communications Technologies ((LNDECT,volume 45))

Abstract

Computer-based learning methods in medical contexts have attracted a great deal of attention recently. Organ transplantation is one of the key areas where prognosis models are being used for predicting the patients’ survival. The only treatment for patients who suffer from liver failure is transplantation. The aim of the present study is to model the patients’ survival prediction as well as to recognize the most significant attributes on survival after liver transplantation. To address the issue of the imbalanced dataset, a combination of two techniques has been considered to evaluate the result; under-sampling and over-sampling techniques. Decision Tree (DT) and K Nearest Neighbor (KNN) models together with Artificial Neural Network (ANN) have been utilized on the dataset separately to define two-year mortality of patients after liver transplantation using the dataset of Iran Ministry of Health and Medical Education (MOHME). By using Genetic Algorithm (GA), it has been shown that 13 attributes have a strong impact on survival prediction in the case of liver transplant recipients. We also compared three classification models using Receiver Operating Characteristic (ROC) curve and other various performance measures. Moreover, findings of the proposed method have improved the results of previous predictions; Using Decision Tree method, roughly in 80% of the transplantation outcomes have been predicted correctly.

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Author information

Authors and Affiliations

  1. School of Industrial Engineering, Iran University of Science and Technology, Tehran, Iran

    Bahareh Kargar, Vahid Gheshlaghi Gazerani & Mir Saman Pishvaee

Authors
  1. Bahareh Kargar
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  2. Vahid Gheshlaghi Gazerani
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  3. Mir Saman Pishvaee
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Corresponding author

Correspondence to Mir Saman Pishvaee .

Editor information

Editors and Affiliations

  1. Institute for Advanced Studies in Basic Science, Zanjan, Iran

    Mahdi Bohlouli

  2. Institute for Advanced Studies in Basic Science, Zanjan, Iran

    Bahram Sadeghi Bigham

  3. Institute for Advanced Studies in Basic Science, Zanjan, Iran

    Zahra Narimani

  4. Institute for Advanced Studies in Basic Science, Zanjan, Iran

    Mahdi Vasighi

  5. Institute for Advanced Studies in Basic Science, Zanjan, Iran

    Ebrahim Ansari

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Kargar, B., Gazerani, V.G., Pishvaee, M.S. (2020). Predicting Liver Transplantation Outcomes Through Data Analytics. In: Bohlouli, M., Sadeghi Bigham, B., Narimani, Z., Vasighi, M., Ansari, E. (eds) Data Science: From Research to Application. CiDaS 2019. Lecture Notes on Data Engineering and Communications Technologies, vol 45. Springer, Cham. https://doi.org/10.1007/978-3-030-37309-2_12

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