ArticleBMC medical informatics and decision making2008
Support vector machine versus logistic regression modeling for prediction of hospital mortality in critically ill patients with haematological malignancies.
Article in BMC medical informatics and decision making, 2008. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 38 papers.
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Who cites it
38 citing papers in PubMed.
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- A Machine Learning Classifier Improves Mortality Prediction Compared With Pediatric Logistic Organ Dysfunction-2 Score: Model Development and Validation.Critical care explorations · 2021Article
- Prediction of Mortality in Surgical Intensive Care Unit Patients Using Machine Learning Algorithms.Frontiers in medicine · 2021Article
- Time series prediction of under-five mortality rates for Nigeria: comparative analysis of artificial neural networks, Holt-Winters exponential smoothing and autoregressive integrated moving average models.BMC medical research methodology · 2020Article
- Development of a Minimally Invasive Screening Tool to Identify Obese Pediatric Population at Risk of Obstructive Sleep Apnea/Hypopnea Syndrome.Bioengineering (Basel, Switzerland) · 2020Article
- Prediction of immune checkpoint inhibition with immune oncology-related gene expression in gastrointestinal cancer using a machine learning classifier.Journal for immunotherapy of cancer · 2020Article
- An evaluation of time series summary statistics as features for clinical prediction tasks.BMC medical informatics and decision making · 2020Article
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7 authors.
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Abstract
backgroundSeveral models for mortality prediction have been constructed for critically ill patients with haematological malignancies in recent years. These models have proven to be equally or more accurate in predicting hospital mortality in patients with haematological malignancies than ICU severity of illness scores such as the APACHE II or SAPS II 1. The objective of this study is to compare the accuracy of predicting hospital mortality in patients with haematological malignancies admitted to the ICU between models based on multiple logistic regression (MLR) and support vector machine (SVM) based models.
methods352 patients with haematological malignancies admitted to the ICU between 1997 and 2006 for a life-threatening complication were included. 252 patient records were used for training of the models and 100 were used for validation. In a first model 12 input variables were included for comparison between MLR and SVM. In a second more complex model 17 input variables were used. MLR and SVM analysis were performed independently from each other. Discrimination was evaluated using the area under the receiver operating characteristic (ROC) curves (+/- SE).
resultsThe area under ROC curve for the MLR and SVM in the validation data set were 0.768 (+/- 0.04) vs. 0.802 (+/- 0.04) in the first model (p = 0.19) and 0.781 (+/- 0.05) vs. 0.808 (+/- 0.04) in the second more complex model (p = 0.44). SVM needed only 4 variables to make its prediction in both models, whereas MLR needed 7 and 8 variables in the first and second model respectively.
conclusionThe discriminative power of both the MLR and SVM models was good. No statistically significant differences were found in discriminative power between MLR and SVM for prediction of hospital mortality in critically ill patients with haematological malignancies.
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