Comparison between artificial neural network and Cox regression model in predicting the survival rate of gastric cancer patients

Zhu, Lucheng; Luo, Wenjun; Su, Meng; Wei, Hangping; Wei, Juan; Zhang, Xuebang; Zou, Changlin

doi:10.3892/br.2013.140

Cited by 49 publications

(35 citation statements)

References 11 publications

(8 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Although the mean AUC values for both models were far from 0.5, the mean AUC for ANN (0.845) was statistically higher for the LLM (0.744). Some studies also showed that ANN performs as well as or better than traditional statistical models (Kurt et al, 2008;Hashemian et al, 2013;Zhu et al, 2013). However some other studies showed that the traditional model outperforms ANN (Xiang et al, 2000).…”

Section: Discussionmentioning

confidence: 99%

“…One advantage of ANNs is that these models may rapidly recognize linear, non linear and/or even interaction effects (Zhu et al, 2013).…”

Section: Discussionmentioning

confidence: 99%

“…Some studies have compared ANN with Cox Model in chronic disease (Hashemian et al, 2013;Zhu et al, 2013). Tangri and et al designed a study to compare the factors that predict survival on peritoneal dialysis using ANN, logistic and Cox regression methods (Tangri et al, 2011).…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Comparison of the Performance of Log-logistic Regression and Artificial Neural Networks for Predicting Breast Cancer Relapse

Faradmal¹,

Soltanian²,

Roshanaei³

et al. 2014

Asian Pacific Journal of Cancer Prevention

View full text Add to dashboard Cite

Section: Discussionmentioning

confidence: 99%

“…One advantage of ANNs is that these models may rapidly recognize linear, non linear and/or even interaction effects (Zhu et al, 2013).…”

Section: Discussionmentioning

confidence: 99%

See 1 more Smart Citation

Comparison of the Performance of Log-logistic Regression and Artificial Neural Networks for Predicting Breast Cancer Relapse

Faradmal¹,

Soltanian²,

Roshanaei³

et al. 2014

Asian Pacific Journal of Cancer Prevention

View full text Add to dashboard Cite

“…The 6 key predictors identified by the DT were confirmed by Logistic Regression Likelihood model, which is a tool of choice in medical statistics for binary classification (Zhu et al 2013;Behera et al 2011;Bouwmeester et al 2012). However, the superiority of the DT model is that it was also able to determine dangerous antibody levels, which conventional statistical methods were not able to provide.…”

Section: Practical Significance Of Using Dt For Predictive Modelling mentioning

confidence: 99%

Machine Learning for Predictive Modelling based on Small Data in Biomedical Engineering

et al. 2015

View full text Add to dashboard Cite

Experimental datasets in bioengineering are commonly limited in size, thus rendering Machine Learning (ML) impractical for predictive modelling. Novel techniques of multiple runs for model development and surrogate data analysis for model validation are suggested for prediction of biomedical outcomes based on small datasets for classification and regression tasks. The proposed framework was applied to designing a Neural Network model for osteoarthritic bone fracture risk stratification, and a Decision Tree model for prediction of antibody-mediated kidney transplant rejection. Despite the small datasets (35 bone specimens and 80 kidney transplants), the two models achieved high accuracy of 98.3% and 85%, respectively.

show abstract

“…Reference [22] reports disparities between treatments effectiveness reported from randomised controlled trials and those achieved in routine clinical practice and population based research which EBM champions may not apply on a patient by patient basis. Thus, in most instances, the knowledge acquired from clinical research studies to design evidence based standards fails to directly address clinical questions regarding what is best for the patient at hand.…”

Section: Emergence Of Evidence Based Medicinementioning

confidence: 99%

Complementarity of Clinician Judgment and Evidence Based Models in Medical Decision Making: Antecedents, Prospects, and Challenges

Zhou

Yiranbon

Antwi

2016

BioMed Research International

View full text Add to dashboard Cite

Early accounts of the development of modern medicine suggest that the clinical skills, scientific competence, and doctors' judgment were the main impetus for treatment decision, diagnosis, prognosis, therapy assessment, and medical progress. Yet, clinician judgment has its own critics and is sometimes harshly described as notoriously fallacious and an irrational and unfathomable black box with little transparency. With the rise of contemporary medical research, the reputation of clinician judgment has undergone significant reformation in the last century as its fallacious aspects are increasingly emphasized relative to the evidence based options. Within the last decade, however, medical forecasting literature has seen tremendous change and new understanding is emerging on best ways of sharing medical information to complement the evidence based medicine practices. This review revisits and highlights the core debate on clinical judgments and its interrelations with evidence based medicine. It outlines the key empirical results of clinician judgments relative to evidence based models and identifies its key strengths and prospects, the key limitations and conditions for the effective use of clinician judgment, and the extent to which it can be optimized and professionalized for medical use.

show abstract

Comparison between artificial neural network and Cox regression model in predicting the survival rate of gastric cancer patients

Cited by 49 publications

References 11 publications

Comparison of the Performance of Log-logistic Regression and Artificial Neural Networks for Predicting Breast Cancer Relapse

Comparison of the Performance of Log-logistic Regression and Artificial Neural Networks for Predicting Breast Cancer Relapse

Machine Learning for Predictive Modelling based on Small Data in Biomedical Engineering

Complementarity of Clinician Judgment and Evidence Based Models in Medical Decision Making: Antecedents, Prospects, and Challenges

Contact Info

Product

Resources

About