ArticleActa informatica medica : AIM : journal of the Society for Medical Informatics of Bosnia & Herzegovina : casopis Drustva za medicinsku informatiku BiH2020
Comparison of Artificial Neural Networks and Logistic Regression for 30-days Survival Prediction of Cancer Patients.
Article in Acta informatica medica : AIM : journal of the Society for Medical Informatics of Bosnia & Herzegovina : casopis Drustva za medicinsku informatiku BiH, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers, 1 of them a synthesis that pooled it.
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Who cites it
12 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Prognostic models for survival predictions in advanced cancer patients: a systematic review and meta-analysis.BMC palliative care · 2025Pooled it
- Review
- End-of-life prognostic models in advanced cancer: a scoping review of model development, validation, and impact.BMC palliative care · 2026Article
- Opportunities and challenges in pooling health-related quality-of-life data for prediction modeling in breast cancer across Europe: lessons from the EORTC BALANCE project.ESMO real world data and digital oncology · 2025Article
- Using artificial intelligence to predict patient outcomes from patient-reported outcome measures: a scoping review.Health and quality of life outcomes · 2025Article
- Article
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- Deciduous tooth biomarkers reveal atypical fetal inflammatory regulation in autism spectrum disorder.iScience · 2023Article
- Risk factor identification and prediction models for prolonged length of stay in hospital after acute ischemic stroke using artificial neural networks.Frontiers in neurology · 2023Article
- Flap failure prediction in microvascular tissue reconstruction using machine learning algorithms.World journal of clinical cases · 2022Article
- Impact of Augmented Intelligence on Utilization of Palliative Care Services in a Real-World Oncology Setting.JCO oncology practice · 2022Article
- Comparative Analysis of Web of Science and Pubmed Indexed Medical Journals Published in Former Yugoslav Countries.Medical archives (Sarajevo, Bosnia and Herzegovina) · 2020Article
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3 authors.
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No grant is acknowledged in the PubMed record.
Abstract
introductionA 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.
aimTo determine the influential factors on survival time of palliative care cancer patients and to compare two statistical methods for better prediction of survival.
methodsOne-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.
resultsSignificantly 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.
conclusionANN provides a more accurate estimation than logistic regression. ANN model is an important statistical method for survival prediction of cancer patients.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.