# Application of Artificial Intelligence Techniques to Predict Survival in Kidney Transplantation: A Review

^{*}

## Abstract

**:**

## 1. Introduction

- They help to extract the factors considered by experts in their field of study when evaluating a situation or making decisions.
- They make it possible to find unknown functional relationships or properties among the entry data.
- They quickly adapt to changing environments with no need to redesign the system if the data are updated or replaced by other data.
- They can handle missing and noisy data.
- They make it possible to find relations and correlations among large amounts of data, and to generate solutions with a high degree of accuracy.

## 2. Results

#### 2.1. Machine Learning Techniques

#### 2.1.1. Decision Trees

#### 2.1.2. Ensemble Methods

#### 2.1.3. Artificial Neural Networks

#### 2.1.4. Support Vector Machines

#### 2.2. Application of Machine Learning Algorithms

#### 2.2.1. Classification of Patient Survival

#### 2.2.2. Modelling the Patient Survival Function

## 3. Discussion

## 4. Future Directions

## 5. Conclusions

## Author Contributions

## Funding

## Conflicts of Interest

## Appendix A

#### Decision Tree

#### Random Forest

#### Artificial Neural Networks

_{i}) according to its importance. These are added together and the signal obtained is passed through an activation function f.

- Input layer: receive data or signals from the environment.
- Output layer: provide the network response to input stimuli.
- Hidden layer: they do not receive or provide information to the environment (internal processing of the net).

- Single-layer network. Only one input layer related to the output layer.
- Multi-layer network (MLP). One or more layers are added between the inputs and outputs, as can be seen in Figure A1. The MLP is a neural network that contains one or more layers of hidden neurons and uses the backpropagation (BP) algorithm [57] to train. This algorithm consists of two phases: feed-forward and feed-back propagation. In the feed-forward phase, the network output is calculated with all fixed weighting values while the input vector is entered from the input layer to the output one through the hidden layers. In the feed-back propagation phase, the error signal is calculated by subtracting the network output value from the expected output value. An error signal originates from an output neuron and spreads backwards layer by layer through the network [76]. It then propagates through the hidden layers to the input layer so that the weighting values are corrected. These two phases are repeated and the learning process is recycled to generate a better approach to the output. The learning process ends when the stop condition is met.

#### Support Vector Machine

## Appendix B

#### Bayesian Belief Networks

#### Conditional Inference Trees

#### Estimated Post-Transplant Survival

#### Information Gain

#### K-Nearest Neighbours

#### Kidney Donor Risk Index

#### Kidney Donor Profile Index

#### Weibull Model

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Authors | Data Source | Population | Methods Used |
---|---|---|---|

Yoo et al., 2017 [7] | Three centers in Korea (1997–2012) | 3117 | Survival decision tree, bagging and random forest |

Mark et al., 2019 [32] | United Network for Organ Sharing (UNOS) (1987–2014) | 100,000 | Combines predictions from random survival forest constructed from conditional inference trees with a Cox proportional hazards model |

Bae et al., 2019 [33] | Organ Procurement and Transplantation Network (OPTN) (2005–2016) | 120,818 kidney recipients and 376,272 candidates on the waiting list | Combinations of The Kidney Donor Profile Index and the Estimated Post Transplant Survival score using random survival forest, and waitlist survival by Estimated Post Transplant Survival score using Weibull regressions |

Atallah et al., 2019 [34] | Urology and Nephrology Center, Mansoura, Egypt (1976–2017) | 2728 | Merges information gain with Naïve Bayes and k-nearest neighbour |

Nematollahi et al., 2017 [35] | Nemazee Hospital, Shiraz, southern Iran (2008–2012) | 717 | Artificial neural network and support vector machines |

Tapak et al, 2017 [31] | Ekbatan or Besaat hospitals, Iran (1994–2011) | 378 | Artificial neuronal networks |

Shahmoradi et al., 2016 [36] | Sina Hospital Urology Research Center, Iran (2007–2013) | 513 | C5.0, artificial neural networks and classification and regression trees |

Luck et al., 2017 [30] | Scientific Registry of Transplant Recipients (2000–2014) | 131,709 | Artificial neural network taking into account two kinds of information loss, the presence of ties and the presence of censoring. |

Topuz et al., 2018 [37] | UNOS (2004–2015) | 31,207 | Feature selection with support vector machine, artificial neural network and bootstrap to construct a Bayesian belief network |

Authors | Method Used | Benchmark | Best Performance | Accuracy | Sensitivity |
---|---|---|---|---|---|

Nematollahi et al., 2017 [35] | ANN and SVM | Logistic regression | SVM | 90.40 | 98.20 |

Tapak et al, 2017 [31] | ANN | Logistic regression | ANN | 75 | 91 |

Shahmoradi et al., 2016 [36] | C5.0, ANN and CART | N/A | C5.0 | 87.21 | 90.85 |

Atallah et al., 2019 [34] | Merges information gain with naïve Bayes and K-NN | J48, Naïve Bayes, ANN, RF, SVM, KNN | Proposed method | 80.77 | 80.40 |

Topuz et al., 2018 [37] | Feature selection with SVM, ANN and bootstrap to construct a Bayesian Belief Network | N/A | Proposed method | 68.40 | 41.00 |

Authors | Method Used | Benchmark | Best Performance | C-Index |
---|---|---|---|---|

Yoo et al., 2017 [7] | Survival decision tree, bagging, and RF | Decision tree and Cox regression | Survival decision tree | 0.80 |

Mark et al., 2019 [32] | Combines predictions from RSF constructed from conditional inference trees with a Cox proportional hazards model | EPTS, Cox model, random forest | Proposed method | 0.724 |

Luck et al., 2017 [30] | Artificial neural network taking into account two kinds of information loss, the presence of ties and the presence of censoring. | Traditional Cox model using Efron’s method | Proposed method | 0.6550 |

Bae et al., 2019 [33] | Combinations of KDPI and EPTS using RSF | KDRI | Proposed method | 0.637 |

Recipient/Donor Factors |
---|

The 3-month serum creatinine level post-transplant |

Recipient age |

Kidney cold ischemic time |

Donor age |

Discharge time creatinine |

Body mass index |

Pre-transplant dialysis |

Recipient functional status at registration |

Recipient diabetes at registration |

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**MDPI and ACS Style**

Díez-Sanmartín, C.; Sarasa Cabezuelo, A.
Application of Artificial Intelligence Techniques to Predict Survival in Kidney Transplantation: A Review. *J. Clin. Med.* **2020**, *9*, 572.
https://doi.org/10.3390/jcm9020572

**AMA Style**

Díez-Sanmartín C, Sarasa Cabezuelo A.
Application of Artificial Intelligence Techniques to Predict Survival in Kidney Transplantation: A Review. *Journal of Clinical Medicine*. 2020; 9(2):572.
https://doi.org/10.3390/jcm9020572

**Chicago/Turabian Style**

Díez-Sanmartín, Covadonga, and Antonio Sarasa Cabezuelo.
2020. "Application of Artificial Intelligence Techniques to Predict Survival in Kidney Transplantation: A Review" *Journal of Clinical Medicine* 9, no. 2: 572.
https://doi.org/10.3390/jcm9020572