Graft survival prediction in liver transplantation using artificial neural network models

Raji, C. G.; Chandra, Satish

doi:10.1016/j.jocs.2016.05.005

Cited by 41 publications

(27 citation statements)

References 18 publications

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“…Similar results have been published in other studies [38,39]. The ANN showed significantly better results in predicting survival of patients than the other models commonly used in different studies [40][41][42].…”

Section: Discussionsupporting

confidence: 88%

Follow-Up and Risk Assessment in Patients with Myocardial Infarction Using Artificial Neural Networks

et al. 2017

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Artificial neural networks (ANNs) are machine learning technique, inspired by the principles found in biological neurons. This technique has been used for prediction and classification problems in many areas of medical signal processing. The aim of this paper was to identify individuals with high risk of death after acute myocardial infarction using ANN. A training dataset for ANN was 1705 consecutive patients who underwent 24-hour ECG monitoring, short ECG analysis, noninvasive beat-to-beat heart-rate variability, and baroreflex sensitivity that were followed for 3 years. The proposed neural network classifier showed good performance for survival prediction: 88% accuracy, 81% sensitivity, 93% specificity, 0.85 -measure, and area under the curve value of 0.77. These findings support the theory that patients with high sympathetic activity (reduced baroreflex sensitivity) have an increased risk of mortality independent of other risk factors and that artificial neural networks can indicate the individuals with a higher risk.

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Section: Discussionsupporting

confidence: 88%

Follow-Up and Risk Assessment in Patients with Myocardial Infarction Using Artificial Neural Networks

et al. 2017

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“…After all, when ML techniques apply to each dataset, the outcome is assessed using different assessment measures to show the better performance of an individual technique. erefore, six assessment measure named MAE [13,18,19], RMSE [8,20,21], RAE [16,22,23], RRSE [22,24], recall [9,10,25], and accuracy [26][27][28] are utilized to evaluate the performance of ML techniques on SDP datasets. We have used error-based assessment measures which are not reported in the literature, while recall and accuracy have been used 3 and 7 times, respectively (Figure 3).…”

Section: Methodology and Techniquesmentioning

confidence: 99%

Software Defect Prediction for Healthcare Big Data: An Empirical Evaluation of Machine Learning Techniques

Khan

Naseem

Shah

et al. 2021

Journal of Healthcare Engineering

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Software defect prediction (SDP) in the initial period of the software development life cycle (SDLC) remains a critical and important assignment. SDP is essentially studied during few last decades as it leads to assure the quality of software systems. The quick forecast of defective or imperfect artifacts in software development may serve the development team to use the existing assets competently and more effectively to provide extraordinary software products in the given or narrow time. Previously, several canvassers have industrialized models for defect prediction utilizing machine learning (ML) and statistical techniques. ML methods are considered as an operative and operational approach to pinpoint the defective modules, in which moving parts through mining concealed patterns amid software metrics (attributes). ML techniques are also utilized by several researchers on healthcare datasets. This study utilizes different ML techniques software defect prediction using seven broadly used datasets. The ML techniques include the multilayer perceptron (MLP), support vector machine (SVM), decision tree (J48), radial basis function (RBF), random forest (RF), hidden Markov model (HMM), credal decision tree (CDT), K-nearest neighbor (KNN), average one dependency estimator (A1DE), and Naïve Bayes (NB). The performance of each technique is evaluated using different measures, for instance, relative absolute error (RAE), mean absolute error (MAE), root mean squared error (RMSE), root relative squared error (RRSE), recall, and accuracy. The inclusive outcome shows the best performance of RF with 88.32% average accuracy and 2.96 rank value, second-best performance is achieved by SVM with 87.99% average accuracy and 3.83 rank values. Moreover, CDT also shows 87.88% average accuracy and 3.62 rank values, placed on the third position. The comprehensive outcomes of research can be utilized as a reference point for new research in the SDP domain, and therefore, any assertion concerning the enhancement in prediction over any new technique or model can be benchmarked and proved.

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“…en, ML techniques are applied to each dataset, and the outcomes are assessed using different assessment measures to show the better performance of an individual technique. Eleven assessment measures named MAE [13,24,25], RMSE [8,26,27], RAE [22,28,29], RRSE [28], specificity [30][31][32], precision [9,15,33], recall [9,10,31], FM [9,15,20], GM [8,34], MCC [9,35,36], and accuracy [37][38][39] are utilized to evaluate the performance of ML techniques on SDP datasets.…”

Section: Methodsmentioning

confidence: 99%

Investigating Tree Family Machine Learning Techniques for a Predictive System to Unveil Software Defects

Naseem¹,

Khan

Ahmad³

et al. 2020

Complexity

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Software defects prediction at the initial period of the software development life cycle remains a critical and important assignment. Defect prediction and correctness leads to the assurance of the quality of software systems and has remained integral to study in the previous years. The quick forecast of imperfect or defective modules in software development can serve the development squad to use the existing assets competently and effectively to provide remarkable software products in a given short timeline. Hitherto, several researchers have industrialized defect prediction models by utilizing statistical and machine learning techniques that are operative and effective approaches to pinpoint the defective modules. Tree family machine learning techniques are well-thought-out to be one of the finest and ordinarily used supervised learning methods. In this study, different tree family machine learning techniques are employed for software defect prediction using ten benchmark datasets. These techniques include Credal Decision Tree (CDT), Cost-Sensitive Decision Forest (CS-Forest), Decision Stump (DS), Forest by Penalizing Attributes (Forest-PA), Hoeffding Tree (HT), Decision Tree (J48), Logistic Model Tree (LMT), Random Forest (RF), Random Tree (RT), and REP-Tree (REP-T). Performance of each technique is evaluated using different measures, i.e., mean absolute error (MAE), relative absolute error (RAE), root mean squared error (RMSE), root relative squared error (RRSE), specificity, precision, recall, F-measure (FM), G-measure (GM), Matthew’s correlation coefficient (MCC), and accuracy. The overall outcomes of this paper suggested RF technique by producing best results in terms of reducing error rates as well as increasing accuracy on five datasets, i.e., AR3, PC1, PC2, PC3, and PC4. The average accuracy achieved by RF is 90.2238%. The comprehensive outcomes of this study can be used as a reference point for other researchers. Any assertion concerning the enhancement in prediction through any new model, technique, or framework can be benchmarked and verified.

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Graft survival prediction in liver transplantation using artificial neural network models

Cited by 41 publications

References 18 publications

Follow-Up and Risk Assessment in Patients with Myocardial Infarction Using Artificial Neural Networks

Follow-Up and Risk Assessment in Patients with Myocardial Infarction Using Artificial Neural Networks

Software Defect Prediction for Healthcare Big Data: An Empirical Evaluation of Machine Learning Techniques

Investigating Tree Family Machine Learning Techniques for a Predictive System to Unveil Software Defects

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