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Comparison of Artificial Neural Networks and Logistic Regression for 30-days Survival Prediction of Cancer Patients
- Source :
- Acta Informatica Medica
- Publication Year :
- 2020
-
Abstract
- Introduction A machine learning technique that imitates neural system and brain can provide better than traditional methods like logistic regression for survival prediction and create an algorithm by determining influential factors. Aim To determine the influential factors on survival time of palliative care cancer patients and to compare two statistical methods for better prediction of survival. Methods One-year data is gathered from the patients that we followed in the palliative care clinic of our hospital (2017-2018) (n = 189). All data were retrospectively evaluated. After descriptive statistics, we used Pearson and Spearman correlations for parametric and non-parametric variables. The Artificial Neural Networks (ANN) and logistic regression model were applied to parameters which have a significant correlation with short survival. Results Significantly correlated variables with short survival were Palliative Performance Scale (PPS), Edmonton Symptom Assessment System (ESAS), Karnofsky Performance Scale (KPS), brain, liver, and distant metastasis, hemogram parameters, cero-reactive protein (CRP) and albumin (ALB). ANN model showed 89.3% prediction accuracy while the logistic regression model showed 73.0%. ANN model achieved a better AUC value of 0.86 than logistic regression model (0.76). Discussion There are several prognostic evaluation tools such as PPS, KPS, CRP, albumin, leukocytes, neutrophil were reported several studies as survival-related parameters in logistic regression models, also. Many studies compare ANN with logistic regression. When we evaluated these parameters totally, we observed the same relations with survival then we used the same parameters in the ANN model. The effectivity of the survival prediction models can be improved with the use of ANN. Conclusion ANN provides a more accurate estimation than logistic regression. ANN model is an important statistical method for survival prediction of cancer patients.
- Subjects :
- 0301 basic medicine
Palliative care
Logistic regression
prognostic estimates
Correlation
03 medical and health sciences
0302 clinical medicine
Statistics
Medicine
survival prediction
Short survival
Parametric statistics
Original Paper
palliative care
Artificial neural network
business.industry
logistic regression
Cancer
General Medicine
medicine.disease
030104 developmental biology
classification
030220 oncology & carcinogenesis
business
artificial neural networks
Predictive modelling
Subjects
Details
- Language :
- English
- Database :
- OpenAIRE
- Journal :
- Acta Informatica Medica
- Accession number :
- edsair.doi.dedup.....0cb4e2713a46cd669470dba794873180