Advantages and disadvantages of using artificial neural networks versus logistic regression for predicting medical outcomes.

By J. Tu. 1996

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Citations in the corpus

Opportunities and obstacles for deep learning in biology and medicine (2017, methodology): “In 1996, Tu [263] compared the effectiveness of artificial neural networks and logistic regression, questioning whether these techniques would replace traditional statistical methods for predicting medical outcomes such as myocardial infarction [264] or mortality [265].”
Applications of machine learning methods for engineering risk assessment – A review (2020, background): “According to Tu (1996), ANNs are also examples of “black box” methods and they cannot explicitly identify possible causal relationships between input and output variables. van Gulijk et al. (2018) suggest that the some “black box” methods in machine learning can make it difficult to trust the…”
A data-driven approach to predicting diabetes and cardiovascular disease with machine learning (2019, background): “The study chose to exclude neural networks from its analysis due to the “black-box” (non-transparency) nature of the approach [23].”
Landslide susceptibility mapping using frequency ratio, logistic regression, artificial neural networks and their comparison: A case study from Kat landslides (Tokat - Turkey) (2009, methodology): “…regression using different data sets, with some researchers finding superior performance for the neural networks and other authors finding no differences in overall predictive performance (Tu, 1996; Schumacher et al., 1996; Manel et al., 1999; Ottenbacher et al., 2001; Mahiny and Turner, 2003).”
Forecasting of chlorophyll-a concentrations in South San Francisco Bay using five different models (2015, background): “hemical pesticides in upstream farms) may control eutrophication t the bays [3].”
Estimation of lignocellulosic biomass pyrolysis product yields using artificial neural networks (2021, background): “Furthermore, ANNs are often computationally expensive and sometimes prone to overfitting, while model development is somehow empirical and methodological issues remain to be resolved [9].”
A Comparison of Logistic Regression, Classification and Regression Tree, and Neural Networks Models in Predicting Violent Re-Offending (2011, background): “2005) and in the broader medical field (Harper 2005; Tu 1996).”
Intelligent Building Control Systems for Thermal Comfort and Energy-Efficiency: A Systematic Review of Artificial Intelligence-Assisted Techniques (2021, methodology): “• Problem of overlearning (learning at the cost of generalization) [171].”