ArticleActa informatica medica : AIM : journal of the Society for Medical Informatics of Bosnia & Herzegovina : casopis Drustva za medicinsku informatiku BiH2015
A Comparison of Logistic Regression Model and Artificial Neural Networks in Predicting of Student's Academic Failure.
Article in Acta informatica medica : AIM : journal of the Society for Medical Informatics of Bosnia & Herzegovina : casopis Drustva za medicinsku informatiku BiH, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
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10 citing papers in PubMed.
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- The use of artificial intelligence models to predict survival in patients with laryngeal squamous cell carcinoma.Scientific reports · 2023Article
- The effectiveness of neurofeedback on the management of academic performance in students with academic failure: experimental research.Annals of medicine and surgery (2012) · 2023Article
- Prediction of gastrointestinal cancers in the ONCONUT cohort study: comparison between logistic regression and artificial neural network.Frontiers in oncology · 2023Article
- Potential Future Directions in Optimization of Students' Performance Prediction System.Computational intelligence and neuroscience · 2022Review
- Exponentially Increasing Trend of Infected Patients with COVID-19 in Iran: A Comparison of Neural Network and ARIMA Forecasting Models.Iranian journal of public health · 2020Article
- Classification and prediction of milk yield level for Holstein Friesian cattle using parametric and non-parametric statistical classification models.Journal of advanced veterinary and animal research · 2020Article
- Mathematical Basis of Predicting Dominant Function in Protein Sequences by a Generic HMM-ANN Algorithm.Acta biotheoretica · 2018Article
- Association of ARID5B Genetic Variants with Risk of Childhood B Cell Precursor Acute Lymphoblastic Leukaemia in LatviaAsian Pacific journal of cancer prevention : APJCP · 2018Article
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2 authors.
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Abstract
BACKGROUND AND
objectiveArtificial Neural Networks (ANNs) have recently been applied in situations where an analysis based on the logistic regression (LR) is a standard statistical approach; direct comparisons of the results, however, are seldom attempted. In this study, we compared both logistic regression models and feed-forward neural networks on the academic failure data set.
methodsThe data for this study included 18 questions about study situation of 275 undergraduate students selected randomly from among nursing and midwifery and paramedic schools of Hormozgan University of Medical Sciences in 2013. Logistic regression with forward method and feed forward Artificial Neural Network with 15 neurons in hidden layer were fitted to the dataset. The accuracy of the models in predicting academic failure was compared by using ROC (Receiver Operating Characteristic) and classification accuracy.
resultsAmong nine ANNs, the ANN with 15 neurons in hidden layer was a better ANN compared with LR. The Area Under Receiver Operating Characteristics (AUROC) of the LR model and ANN with 15 neurons in hidden layers, were estimated as 0.55 and 0.89, respectively and ANN was significantly greater than the LR. The LR and ANN models respectively classified 77.5% and 84.3% of the students correctly.
conclusionBased on this dataset, it seems the classification of the students in two groups with and without academic failure by using ANN with 15 neurons in the hidden layer is better than the LR model.
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