Five Years Survival of Patients After Liver Transplantation and Its Effective Factors by Neural Network and Cox Poroportional Hazard Regression Models


avatar Bahareh Khosravi 1 , avatar Saeedeh Pourahmad ORCID 1 , 2 , * , avatar Amin Bahreini 3 , avatar Saman Nikeghbalian 4 , avatar Goli Mehrdad 5

Department of Biostatistics, Shiraz University of Medical Sciences, Shiraz, IR Iran
Colorectal Research Center, Shiraz University of Medical Sciences, Shiraz, IR Iran
Department of Organ Transplantation, Ahvaz University of Medical Sciences, Ahvaz, IR Iran
Department of Organ Transplantation, Shiraz University of Medical Sciences, Shiraz, IR Iran
Center of Namazee Hospital Organ Transplant, Shiraz, IR Iran

how to cite: Khosravi B, Pourahmad S, Bahreini A, Nikeghbalian S, Mehrdad G. Five Years Survival of Patients After Liver Transplantation and Its Effective Factors by Neural Network and Cox Poroportional Hazard Regression Models. Hepat Mon. 2015;15(9):e25164. doi: 10.5812/hepatmon.25164.



Transplantation is the only treatment for patients with liver failure. Since the therapy imposes high expenses to the patients and community, identification of effective factors on survival of such patients after transplantation is valuable.


The current study attempted to model the survival of patients (two years old and above) after liver transplantation using neural network and Cox Proportional Hazards (Cox PH) regression models. The event is defined as death due to complications of liver transplantation.

Patients and Methods:

In a historical cohort study, the clinical findings of 1168 patients who underwent liver transplant surgery (from March 2008 to march 2013) at Shiraz Namazee Hospital Organ Transplantation Center, Shiraz, Southern Iran, were used. To model the one to five years survival of such patients, Cox PH regression model accompanied by three layers feed forward artificial neural network (ANN) method were applied on data separately and their prediction accuracy was compared using the area under the receiver operating characteristic curve (ROC). Furthermore, Kaplan-Meier method was used to estimate the survival probabilities in different years.


The estimated survival probability of one to five years for the patients were 91%, 89%, 85%, 84%, and 83%, respectively. The areas under the ROC were 86.4% and 80.7% for ANN and Cox PH models, respectively. In addition, the accuracy of prediction rate for ANN and Cox PH methods was equally 92.73%.


The present study detected more accurate results for ANN method compared to those of Cox PH model to analyze the survival of patients with liver transplantation. Furthermore, the order of effective factors in patients’ survival after transplantation was clinically more acceptable. The large dataset with a few missing data was the advantage of this study, the fact which makes the results more reliable.

1. Background

Liver transplantation is a standard and high survival treatment in patients with advanced failure and irreversible liver (1-3). To determine the best time for liver transplantation as well as the independent prognostic factors for survival of the candidates are considered as the debatable subjects in this field (4). In Iran, the first liver transplantation was performed in Namazee Hospital, Shiraz, Iran, in 1993 (5). Nowadays, liver transplantation is commonly executed worldwide. The survival rate analysis of the patients with liver transplant may help clinicians to reach the best possible decision in pre and post-operative cares with neutrality (5-10). There were various methods to analyze the survival phenomena. Classic statistical methods need to consider some underlying assumptions on data structure. For instance, Cox Proportional Hazard (Cox PH) regression assumes that predictors have similar impacts on survival over time, and hazard rate is also non-dependent of the predictors (11). Usually, such mentioned assumptions do not consist the structure of data and this fact may limit the application of these methods. In recent years, soft computing methods help classical methods to model the unstructured huge data set (12-19). These methods do not have any theoretical assumptions on data structure (20). And since they model the relations among the data by learning their patterns they work better on larger data set. For instance, artificial neural network (ANN) is a method similar to a box that consists of layers and small units in each layer are called neuron. Neurons are combined in each layer by defining the initial weights. Updating the weights among neurons of a layer and the connections between two adjacent layers are the topics which will be dealt with in learning process. Indeed, the network learns the relations between input and output data (20). Modeling the survival of clinical phenomena using ANN method has been applied in previous researches for different diseases (21-28) and also for liver disease (6-10, 29, 30). However, the definition of inputs and outputs, the structure of the networks, their learning algorithms and the population study are different in liver disease studies. Furthermore, the comparison among survival modeling methods of liver transplant data has been studied less in previous researches (5-7, 10, 29, 30).

2. Objectives

The current study aimed to model the survival of patients over two years old after liver transplantation surgery using Cox PH regression and ANN models. The order of important factors on survival after transplantation was derived for both models. Furthermore, one to five survival probabilities of the patients were estimated by Kaplan-Meier method. To compare the performance of the models, the area under the ROC curve, accuracy rate, sensitivity and specificity values, positive and negative predictive values and the Youden index J (31) were considered as the index.

3. Materials and Methods

A census based on a historical cohort study was done to collect the clinical findings of 1361 patients over two years old who underwent liver transplant surgery from March 2008 to March 2013 at Shiraz Namazee Hospital Organ Transplantation Center, Shiraz, Southern Iran. Almost the majority of patients over the country are referred to this center for liver transplantation. Patients were excluded if transplanted more than once, survived less than one-day or missing some important data. Accordingly, the total sample size decreased to 1168 subjects. Thirty-seven features including recipient age, recipient weight, lung complication after transplantation, comorbidity disease, exploration after transplantation, Cytomegalovirus (CMV) infection, diabetes after transplantation, duration of hospital stay, vascular complication after transplantation, pack cell (PC), etc. were considered as the input features (variables) for each patient.

4. Results

In the current study, 1168 patients with a Mean ± SD age of 32.4 ± 17.6 years including 734 (62.8%) males and 434 (37.2%) females participated. Patients were followed up for a period of five years after the liver transplantation in order to observe the event died due to complications from liver transplantation. Among them, 129 (11%) patients died due to complications from liver transplantation and 1039 (89%) patients were alive or missed in the follow up (censored data). Tables 1 and 2 describe the patients regarding their qualitative and quantitative features. The mean survival time for these patients was 52 months and 18 days with a standard deviation of 18 days. One to five years estimated survival probability of the patients were 91%, 89%, 85%, 84% and 83%, respectively by Kaplan-Meier method (Table 3).

Table 1. Description of Qualitative Input Features for All Patients in the Study a
Variable NameNo. (%)
Recipient gender
Male734 (62.80)
Female434 (37.20)
Recipient diagnosis
Metabolic180 (15.40)
Cholestatic 233 (19.90)
Hepatitis455 (39.00)
Tumors16 (1.40)
Cryptogenic71 (6.10)
Other causes213 (18.2)
Comorbidity disease b
No1011 (86.6)
Yes157 (13.4)
MELD and PELD score
< 20619 (53.00)
> 20549 (47.00)
A130 (11.1)
B506 (43.3)
C532 (45.5)
No1022 (87.5)
Yes146 (12.5)
No1119 (95.8)
Yes49 (4.2)
Whole organ980 (83.9)
Split63 (5.4)
Living 125 (10.7)
No1156 (99.00)
Yes12 (1.00)
No1099 (94.10)
Yes69 (5.90)
No918 (78.60)
Yes250 (21.4)
No1104 (94.50)
Yes64 (5.50)
No1065 (91.20)
Yes103 (8.80)
Lung complication after transplantation d
No 1127 (96.50)
Yes 41 (3.50)
Bile duct complication after transplantation
No1099 (94.10)
Yes69 (5.90)
Exploration after transplantation
No958 (82.00)
Yes210 (18.00)
Acute rejection
No 693 (59.30)
Yes 475 (40.70)
Chronic rejection
No1132 (96.9)
Yes 36 (3.10)
Relapse e
No 1140 (97.6)
Yes 28 (2.40)
No 1151 (98.50)
Yes 17 (1.5)
Donor gender
Male 786 (67.30)
Female 382 (32.70)
Donor cause of death
Living 127 (10.90)
Trauma651 (55.70)
CVA292 (25.00)
Other98 (8.40)
Table 2. Description of Quantitative Input features for All Patients in the Study
Variable Name Values a
Recipient age, yr32.39 ± 17.59
Recipient weight, kg59.12 ± 22.30
CHILD Score9.16 ± 2.20
Waiting list time, d162.27 ± 222.61
Creatinine, mg/dL0.86 ± 0.58
INR, IU b2.03 ± 1.28
Total bilirubin, mg/dL8.27 ± 10.27
Cold ischemia time, h6.69 ± 3.50
Warm ischemia time, h0.81 ± 0.22
Pack cell, bag2.40 ± 2.89
Fresh frozen plasma, bag3.10 ± 3.97
Total bleeding, mL1700 ± 1679.38
Duration of operation, h6.01 ± 1.27
Duration of hospital stay, d13.81 ± 7.86
Donor age, yr30.76 ± 14.19
Table 3. One to Five Years Survival Probabilities of the Patients With Transplant
Follow Up Period, yrTotal Data (n = 1168) a
First0.909 ± 0.009
Second0.888 ± 0.010
Third0.854 ± 0.014
Fourth0.843 ± 0.016
Fifth0.833 ± 0.018

Firstly, the data were randomly divided into two parts called training and testing subsets (934 and 234 patients, respectively). The result of log-rank test revealed no significant difference between the survival curves for training and testing subsets (P value = 0.411). Secondly, ANN was trained and evaluated using these two data subsets. Learning procedure including training and testing were conducted for 576 different ANN architectures and the most appropriate ANN was selected based on the area under the ROC = 0.864 (P < 0.0001) for the selected model (Table 4). Based on the Youden criterion cut point, the prediction rate accuracy of this method, was 90.2% (Table 4). The effective factors on survival after liver transplantation were selected based on absolute values of the mean weights for each variable. Tables 5 and 6 show the order of variables in the selected ANN model (sixteen variables). At the end, the assumptions of Cox PH model were checked and fitted to the test data. The best model was selected using the backward conditional method. The area under the ROC for this model was 0.806, which was statistically significant (P < 0.0001) (Table 4). In addition, based on the cut point of Youden criterion, the prediction rate accuracy of Cox PH model was 90.2% (Table 4). Other criteria such as sensitivity, specificity, negative and positive predictive values and the Youden index J were also calculated to offer more opportunities to compare models (Table 4). Some of these criteria were in favor of ANN and some others in favor of Cox PH model. Furthermore, based on the results of Cox PH model on all 1168 patients, 16 variables out of 37 were effective to predict survival in liver transplantation. Indeed, the coefficients of these variables were significant at 0.1 level. Tables 5 and 6 show the order of variables in Cox PH model compared with those of the ANN model.

Table 4. Comparison Criteria for Both Models on Testing Set a
Comparison CriteriaArtificial Neural NetworkCox Proportional Hazards
AUC (Stand. Error)0.864 (0.043) b0.806 (0.067) b
Youden index J0.5880.576
Sensitivity c78.365.2
Specificity c80.692.4
Positive predictive value c30.548.4
Negative predictive value c97.196.1
True prediction d
Survival170 (97.1)195 (96.1)
Dead41 (69.5)16 (51.6)
Total211 (90.2)211 (90.2)
Table 5. The Order of Important Variables Based on Cox Proportionsl Hazardregression a
VariablesCox Regression Model
BetaSEP Value
Type liver b< 0.001
Whole organ-3.5631.1410.002
PNF b2.9110.439< 0.001
Chronic rejection b1.8220.299< 0.001
Renal failure after transplantation b1.6020.232< 0.001
Lung complication after transplantation b1.5990.239< 0.001
Exploration after transplantation b0.9030.222< 0.001
Acute rejection b-0.6460.2040.002
Diabetes after transplantation b-0.7970.2830.005
Duration of hospital stay b-0.0330.0120.006
Vascular complication after transplantation b0.6980.2830.013
PC b0.6980.2830.013
MELD and PELD score b0.4140.1970.035
Donor cause of death b0.036
Recipient age b0.0190.0090.046
Donor age c0.0150.0080.053
Recipient weight c-0.0120.0080.101
Table 6. The Order of Important Variables Based on ANN Models a
VariablesANN Model
Mean Weights
Recipient age-0.214
Previous abdominal surgery before transplantation0.152
Lung complication after transplantation-0.133
Comorbidity disease-0.122
Exploration after transplantation0.112
CMV infection0.107
Renal failure after transplantation-0.105
Acute rejection0.103
CHILD score-0.103
Duration of operation0.096
Recipient diagnosis-0.095
Chronic rejection-0.094

5. Discussion

Liver transplantation is the only treatment for patients with liver failure (5). Without liver transplantation the patients do not have any chance for prolonged survival. During the last two decades the five-year survival of liver transplant patients increased (34). Liver transplant program was established since 20 years ago in Shiraz Namazee Hospital and it is well developed; therefore, more than six hundred liver transplants are done annually. With the increasing number of patients with liver transplant in Iran, the follow up is very important. Indeed, the important features which affect the survival of patients after the surgery is valuable for pre and post-operative cares. But so far, few rigorous studies are conducted on survival in patients with liver transplant (5-10, 29, 30).

Since this phenomena is affected by the regional status, diet or cultural traditions in life, conducting a comprehensive study which encompasses all age groups is needed for the Iranian population. However, some previous studies were conducted on survival of patients with liver transplantation in Iran which utilized classical methods for analysis (5, 10) with the limited age range.

The current study aimed to model the survival of patients with liver transplant in a wide age range (two years old and above) using ANN and Cox PH regression models in order to compare the performance of these two methods to predict death due to complications of liver transplantation. Accordingly, some variables that influenced survival of the patients with liver transplant were selected based on a few studies conducted on survival of such patients and also the experience of more than two thousand liver transplant surgeries in Shiraz transplant center.

The results of the current study revealed that ANN was better than the Cox PH regression model to predict survival in patients with liver transplant based on the area under the ROC curve (Table 4). However, both of them were large enough to be statistically significant (P < 0.0001). In addition, the prediction rate accuracy was similar in both models (Table 4). Furthermore, the Youden index J, sensitivity and negative predictive values were in favor of ANN while specificity and positive predictive values were higher in Cox PH model. However, the significance of the input variables order should be considered, clinically (Tables 5 and 6). Based on the clinical experience, the order of variables in Cox PH model may be more consistent with clinical findings. Although, the recipient age can be an important variable but Primary non function (PNF), lung, kidney and vascular complications have more important effect on the patients’ survival. According to the ANN results in Tables 5 and 6, some variables such as Cold ischemia time (CIT), previous abdominal surgery or transfusion of fresh frozen plasma (FFP), are at the top of the list while clinically they do not seem to be as important as PNF or vascular complications. However, many studies compared these methods for survival analysis in various diseases worldwide (21, 27). All these studies mentioned the superiority of ANN over Cox PH regression in real clinical datasets. In addition, the comparison between these two methods on a simulated dataset confirmed the high ability of ANN method in modeling complex relations compared to that of Cox PH regression model especially for a dataset with high censorship (35).

Generally, comparing these two models, Cox PH model needs to fulfill some theoretical assumptions on data structure. In addition, it uses a subset of variables in the final model (the significant ones). Therefore, its results are easy to interpret and the odds ratio and related confidence intervals can be calculated. In comparison, ANN requires a large data set to learn the relations. In addition, it uses all input variables in modeling process and the absolute value of their weights indicates the importance. Therefore, it cannot distinguish the confounding variables (inconsequential ones) but is a powerful tool to find the complex patterns among the inputs without any assumptions for data structure.

The strength of this study was the possibility of comparison between the two methods in survival analysis of liver transplantation data. In addition, investigating the role of many different factors in survival of patients with liver transplantation, simultaneously among a wide age range and in a large data set, was another advantage of the present research. However, the current study had a potential limitation. Incomplete registration information in the hospital records of patients was problematic. Furthermore, some patients were not available to record their final status (dead/alive). This fact led to lose some subjects. Therefore, the improvement of hospital registration information may be necessary. Moreover, examining other learning algorithms for ANN method or utilizing the hybrid methods between ANN and genetic algorithm are suggested for future studies.



  • 1.

    Abramson O, Rosenthal P. Current status of pediatric liver transplantation. Clin Liver Dis. 2000; 4 (3) : 533 -52 [PubMed]

  • 2.

    Ghobrial RM, Farmer DG, Amersi F, Busuttil RW. Advances in pediatric liver and intestinal transplantation. Am J Surg. 2000; 180 (5) : 328 -34 [PubMed]

  • 3.

    Adam R, Cailliez V, Majno P, Karam V, McMaster P, Caine RY, et al. Normalised intrinsic mortality risk in liver transplantation: European Liver Transplant Registry study. Lancet. 2000; 356 (9230) : 621 -7 [PubMed]

  • 4.

    Hoofnagle JH, Lombardero M, Zetterman RK, Lake J, Porayko M, Everhart J, et al. Donor age and outcome of liver transplantation. Hepatology. 1996; 24 (1) : 89 -96 [DOI][PubMed]

  • 5.

    Sabet B, Rajaee-fard A, Nikeghbalian S, Malek-Hosseini SA. Six years liver transplants outcome in Shiraz transplant center. J of Isfahan Med School. 2009; 27 (99) : 543 -50

  • 6.

    Marsh JW, Finkelstein SD, Demetris AJ, Swalsky PA, Sasatomi E, Bandos A, et al. Genotyping of hepatocellular carcinoma in liver transplant recipients adds predictive power for determining recurrence-free survival. Liver Transpl. 2003; 9 (7) : 664 -71 [DOI][PubMed]

  • 7.

    Hoot NR. Models to Predict Survival After Liver Transplantation. 2005;

  • 8.

    Piscaglia F, Cucchetti A, Benlloch S, Vivarelli M, Berenguer J, Bolondi L, et al. Prediction of significant fibrosis in hepatitis C virus infected liver transplant recipients by artificial neural network analysis of clinical factors. Eur J Gastroenterol Hepatol. 2006; 18 (12) : 1255 -61 [DOI][PubMed]

  • 9.

    Cruz-Ramírez M, Hervás-Martínez C, Gutiérrez PA, Pérez-Ortiz M, Briceño J, de la Mata M. Memetic Pareto differential evolutionary neural network used to solve an unbalanced liver transplantation problem. Soft Computing. 2012; 17 (2) : 275 -84 [DOI]

  • 10.

    Haseli N, Hassanzadeh J, Dehghani SM, Bahador A, Malek Hosseini SA. Long-term survival and its related factors in pediatric liver transplant recipients of shiraz transplant center, shiraz, iran in 2012. Hepat Mon. 2013; 13 (7)[DOI][PubMed]

  • 11.

    Kleinbaum D, Klein M. Survival Analysis: A self-learning text, 2005. 2011;

  • 12.

    Chowdhury DR, Chatterjee M, Samanta RK. An artificial neural network model for neonatal disease diagnosis. Int J Artif Intelligence and Expert Syst . 2011; 2 (3) : 96 -106

  • 13.

    Erguzel TT, Ozekes S, Tan O, Gultekin S. Feature Selection and Classification of Electroencephalographic Signals An Artificial Neural Network and Genetic Algorithm Based Approach. Clin EEG Neurosci. 2014; 14

  • 14.

    Punia R, Singh S. Review on Machine Learning Techniques for Automatic Segmentation of Liver Images. Int J Adv Res In Comput Sci Softw Eng. 2013; 3 (4) : 666 -70

  • 15.

    Motalleb G. Artificial neural network analysis in preclinical breast cancer. Cell J. 2014; 15 (4) : 324 -31 [PubMed]

  • 16.

    Nakajima K, Matsuo S, Wakabayashi H, Yokoyama K, Bunko H, Okuda K, et al. Diagnostic Performance of Artificial Neural Network for Detecting Ischemia in Myocardial Perfusion Imaging. Circ J. 2015; 79 (7) : 1549 -56 [DOI][PubMed]

  • 17.

    Gholipour C, Rahim F, Fakhree A, Ziapour B. Using an Artificial Neural Networks (ANNs) Model for Prediction of Intensive Care Unit (ICU) Outcome and Length of Stay at Hospital in Traumatic Patients. J Clin Diagn Res. 2015; 9 (4) : OC19 -23 [DOI][PubMed]

  • 18.

    Mala K, Sadasivam V, Alagappan S. Neural network based texture analysis of CT images for fatty and cirrhosis liver classification. Appl Soft Comput. 2015; 32 : 80 -6 [DOI]

  • 19.

    Hussain MA, Ansari TM, Gawas PS, Chowdhury NN. Lung Cancer Detection Using Artificial Neural Network & Fuzzy Clustering. Int J Adv Res In Comput Sci Softw Eng. 2015; 4 (3) : 360 -3

  • 20.

    Pourahmad S, Azad M, Paydar S. Diagnosis of Malignancy in Thyroid Tumors by Multi-Layer Perceptron Neural Networks With Different Batch Learning Algorithms. Glob J Health Sci. 2015; 7 (6) : 43378 [DOI][PubMed]

  • 21.

    Jones AS, Taktak AGF, Helliwell TR, Fenton JE, Birchall MA, Husband DJ, et al. An artificial neural network improves prediction of observed survival in patients with laryngeal squamous carcinoma. Eur Arch Otorhinolaryngol. 2006; 263 (6) : 541 -7 [DOI]

  • 22.

    Continuous and Discrete Time Survival Analysis: Neural Network Approaches. Annual International Conference of the IEEE Engineering in Medicine and Biology Society IEEE Engineering in Medicine and Biology Society Annual Conference. : 5420 -3

  • 23.

    Chi CL, Street WN, Wolberg WH. Application of artificial neural network-based survival analysis on two breast cancer datasets. AMIA Annu Symp Proc. 2007; : 130 -4 [PubMed]

  • 24.

    Amiri Z, Mohammad K, Mahmoudi M, Zeraati H, Fotouhi A. Assessment of gastric cancer survival: using an artificial hierarchical neural network. Pak J Biol Sci. 2008; 11 (8) : 1076 -84 [PubMed]

  • 25.

    Biglarian A, Hajizadeh E, Kazemnejad A, Zali M. Application of artificial neural network in predicting the survival rate of gastric cancer patients. Iran J Public Health. 2011; 40 (2) : 80 -6 [PubMed]

  • 26.

    Ansari D, Nilsson J, Andersson R, Regner S, Tingstedt B, Andersson B. Artificial neural networks predict survival from pancreatic cancer after radical surgery. Am J Surg. 2013; 205 (1) : 1 -7 [DOI][PubMed]

  • 27.

    Sedehi M, Amani F, Momeni Dehaghi F. Analysis of survival data of patient with breast cancer using artificial neural network and cox regression models. J of Zabol Univ Of Med Sci and Health Serv. 2014; 5 (4) : 24 -32

  • 28.

    Sharaf T, Tsokos CP. Two Artificial Neural Networks for Modeling Discrete Survival Time of Censored Data. Adv in Artificial Intel. 2015; 2015

  • 29.

    Cucchetti A, Piscaglia F, Grigioni AD, Ravaioli M, Cescon M, Zanello M, et al. Preoperative prediction of hepatocellular carcinoma tumour grade and micro-vascular invasion by means of artificial neural network: a pilot study. J Hepatol. 2010; 52 (6) : 880 -8 [DOI][PubMed]

  • 30.

    Ho WH, Lee KT, Chen HY, Ho TW, Chiu HC. Disease-free survival after hepatic resection in hepatocellular carcinoma patients: a prediction approach using artificial neural network. PLoS One. 2012; 7 (1)[DOI][PubMed]

  • 31.

    Ruopp MD, Perkins NJ, Whitcomb BW, Schisterman EF. Youden Index and optimal cut-point estimated from observations affected by a lower limit of detection. Biom J. 2008; 50 (3) : 419 -30 [DOI][PubMed]

  • 32.

    Zare A, Mahmoodi M, Mohammad K, Zeraati H, Hosseini M, Holakouie Naieni K. A comparison between Kaplan-Meier and weighted Kaplan-Meier methods of five-year survival estimation of patients with gastric cancer. Acta Med Iran. 2014; 52 (10) : 764 -7 [PubMed]

  • 33.

    Goel MK, Khanna P, Kishore J. Understanding survival analysis: Kaplan-Meier estimate. Int J Ayurveda Res. 2010; 1 (4) : 274 -8 [DOI][PubMed]

  • 34.

    Tome S, Wells JT, Said A, Lucey MR. Quality of life after liver transplantation. A systematic review. J Hepatol. 2008; 48 (4) : 567 -77 [DOI][PubMed]

  • 35.

    Biglarian A, Bakhshi E, Baghestani AR, Gohari MR, Rahgozar M, Karimloo M. Nonlinear survival regression using artificial neural network. J Probab Stat. 2013; 2013 : 1 -7

Copyright © 2015, Kowsar Corp. This is an open-access article distributed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License ( which permits copy and redistribute the material just in noncommercial usages, provided the original work is properly cited.