TY  - JOUR
A1  - Lötsch, Jörn
A1  - Ultsch, Alfred
T1  - Pitfalls of using multinomial regression analysis to identify class-structure relevant variables in biomedical datasets: why a mixture of experts (MOE) approach is better
T2  - Preprints
N2  - Recent advances in mathematical modelling and artificial intelligence have challenged the use of traditional regression analysis in biomedical research. This study examined artificial and cancer research data using binomial and multinomial logistic regression and compared its performance with other machine learning models such as random forests, support vector machines, Bayesian classifiers, k-nearest neighbours and repeated incremental clipping (RIPPER). The alternative models often outperformed regression in accurately classifying new cases. Logistic regression had a structural problem similar to early single-layer neural networks, which limited its ability to identify variables with high statistical significance for reliable class assignment. Therefore, regression is not always the best model for class prediction in biomedical datasets. The study emphasises the importance of validating selected models and suggests that a mixture of experts approach may be a more advanced and effective strategy for analysing biomedical datasets.
KW  - data science
KW  - artificial intelligence
KW  - machine-learning
KW  - digital medicine
Y1  - 2023
UR  - http://publikationen.ub.uni-frankfurt.de/frontdoor/index/index/docId/75583
UR  - https://nbn-resolving.org/urn:nbn:de:hebis:30:3-755834
VL  - 2023
IS  - 2023081191
SP  - 1
EP  - 16
ER  -