A Comparison of Logistic Regression, Neural Networks, and Classification Trees Predicting Success of Actuarial Students

Schumacher, Phyllis; Olinsky, Alan; Quinn, J.; Smith, Rachel

doi:10.1080/08832320903449477

Cited by 25 publications

(16 citation statements)

References 8 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The reason is that the training makes the model perfectly fits the data set. Thus with new data sets, the prediction might be poor [24]. Ayer et al [27] compared the two methods in several aspects: LR requires more statistical knowledge than ANN.…”

Section: Artificial Neural Network (Ann)mentioning

confidence: 99%

Prediction of in-hospital mortality in patients on mechanical ventilation post traumatic brain injury: machine learning approach

Abujaber

Fadlalla

Gammoh

et al. 2020

BMC Med Inform Decis Mak

View full text Add to dashboard Cite

Background The study aimed to introduce a machine learning model that predicts in-hospital mortality in patients on mechanical ventilation (MV) following moderate to severe traumatic brain injury (TBI). Methods A retrospective analysis was conducted for all adult patients who sustained TBI and were hospitalized at the trauma center from January 2014 to February 2019 with an abbreviated injury severity score for head region (HAIS) ≥ 3. We used the demographic characteristics, injuries and CT findings as predictors. Logistic regression (LR) and Artificial neural networks (ANN) were used to predict the in-hospital mortality. Accuracy, area under the receiver operating characteristics curve (AUROC), precision, negative predictive value (NPV), sensitivity, specificity and F-score were used to compare the models` performance. Results Across the study duration; 785 patients met the inclusion criteria (581 survived and 204 deceased). The two models (LR and ANN) achieved good performance with an accuracy over 80% and AUROC over 87%. However, when taking the other performance measures into account, LR achieved higher overall performance than the ANN with an accuracy and AUROC of 87% and 90.5%, respectively compared to 80.9% and 87.5%, respectively. Venous thromboembolism prophylaxis, severity of TBI as measured by abbreviated injury score, TBI diagnosis, the need for blood transfusion, heart rate upon admission to the emergency room and patient age were found to be the significant predictors of in-hospital mortality for TBI patients on MV. Conclusions Machine learning based LR achieved good predictive performance for the prognosis in mechanically ventilated TBI patients. This study presents an opportunity to integrate machine learning methods in the trauma registry to provide instant clinical decision-making support.

show abstract

Section: Artificial Neural Network (Ann)mentioning

confidence: 99%

Prediction of in-hospital mortality in patients on mechanical ventilation post traumatic brain injury: machine learning approach

Abujaber

Fadlalla

Gammoh

et al. 2020

BMC Med Inform Decis Mak

View full text Add to dashboard Cite

show abstract

“…The training continues until the error is no further reducible [27]. Once trained, the ANN can be used for future cases where the outcome is unknown [28].…”

Section: Artificial Neural Network (Ann)mentioning

confidence: 99%

Prediction of in-hospital mortality in patients with post traumatic brain injury using National Trauma Registry and Machine Learning Approach

Abujaber

Fadlalla

Gammoh

et al. 2020

Scand J Trauma Resusc Emerg Med

View full text Add to dashboard Cite

Background: The use of machine learning techniques to predict diseases outcomes has grown significantly in the last decade. Several studies prove that the machine learning predictive techniques outperform the classical multivariate techniques. We aimed to build a machine learning predictive model to predict the in-hospital mortality for patients who sustained Traumatic Brain Injury (TBI). Methods: Adult patients with TBI who were hospitalized in the level 1 trauma center in the period from January 2014 to February 2019 were included in this study. Patients' demographics, injury characteristics and CT findings were used as predictors. The predictive performance of Artificial Neural Networks (ANN) and Support Vector Machines (SVM) was evaluated in terms of accuracy, Area Under the Curve (AUC), sensitivity, precision, Negative Predictive Value (NPV), specificity and F-score. Results: A total of 1620 eligible patients were included in the study (1417 survival and 203 non-survivals). Both models achieved accuracy over 91% and AUC over 93%. SVM achieved the optimal performance with accuracy 95.6% and AUC 96%. Conclusions: for prediction of mortality in patients with TBI, SVM outperformed the well-known classical models that utilized the conventional multivariate analytical techniques.

show abstract

“…The study in (Dobbin and Simon, 2007) develops probability models and the paper proposed the sample size determination for prediction in the context of high-dimensional data that captures variability in both steps of predictor development. Predictive models are found useful in exploring the educational data as reported in (Osmanbegović and Suljić, 2012); (Schumacher et al, 2010) and (Bidgoli et al, 2003) for the predictions of student's performance. Statistical methods for estimating dataset size requirements and classification of microarray data using learning curves is proposed in (Mukherjee et al, 2003).…”

Section: Related Workmentioning

confidence: 99%

Evaluating the Effect of Dataset Size on Predictive Model Using Supervised Learning Technique

Ajiboye

Arshah

Qin

et al. 2015

ijsecs

View full text Add to dashboard Cite

Learning models used for prediction purposes are mostly developed without paying much cognizance to the size of datasets that can produce models of high accuracy and better generalization. Although, the general believe is that, large dataset is needed to construct a predictive learning model. To describe a data set as large in size, perhaps, is circumstance dependent, thus, what constitutes a dataset to be considered as being big or small is vague. In this paper, the ability of the predictive model to generalize with respect to a particular size of data when simulated with new untrained input is examined. The study experiments on three different sizes of data using Matlab program to create predictive models with a view to establishing if the size of data has any effect on the accuracy of a model. The simulated output of each model is measured using the Mean Absolute Error (MAE) and comparisons are made. Findings from this study reveals that, the quantity of data partitioned for the purpose of training must be of good representation of the entire sets and sufficient enough to span through the input space. The results of simulating the three network models also shows that, the learning model with the largest size of training sets appears to be the most accurate and consistently delivers a much better and stable results.

show abstract

A Comparison of Logistic Regression, Neural Networks, and Classification Trees Predicting Success of Actuarial Students

Cited by 25 publications

References 8 publications

Prediction of in-hospital mortality in patients on mechanical ventilation post traumatic brain injury: machine learning approach

Prediction of in-hospital mortality in patients on mechanical ventilation post traumatic brain injury: machine learning approach

Prediction of in-hospital mortality in patients with post traumatic brain injury using National Trauma Registry and Machine Learning Approach

Evaluating the Effect of Dataset Size on Predictive Model Using Supervised Learning Technique

Contact Info

Product

Resources

About