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How to improve logistic regression model in R?

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Markov chain Monte Carlo

Multinomial logistic regression

Logistic regression

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Open access•Journal Article•DOI Predicting Motor Insurance Claims Using Telematics Data—XGBoost versus Logistic Regression Jessica Pesantez-Narvaez, Montserrat Guillén, Manuela Alcañiz - Show less +2 more 20 Jun 2019 83 Citations	Our findings showed that logistic regression is a suitable model given its interpretability and good predictive capacity.
Proceedings Article•DOI Framework for integration of domain knowledge into logistic regression Sandro Radovanovic, Boris Delibašić, Milos Jovanovic, Milan Vukicevic, Milija Suknović - Show less +4 more 25 Jun 2018 7 Citations	We show that the proposed framework yields better results compared to standard logistic regression model.
Journal Article•DOI Modeling categorical variables by logistic regression. Chao-Ying Joanne Peng, Barbara D. Manz, Juanita Keck - Show less +2 more 01 May 2001-American Journal of Health Behavior 37 Citations	Logistic regression is useful for health-related research in which outcomes of interest are often categorical.
Open access•Journal Article•DOI An MCMC model search algorithm for regression problems Athanassios Petralias, Petros Dellaportas - Show less +1 more 09 Sep 2013-Journal of Statistical Computation and Simulation 19 Citations	In a series of examples with linear and logistic regression, we report evidence that our proposed algorithm performs better than the existing algorithms.
Journal Article•DOI Logistic regression model for predicting the failure probability of a landslide dam Jia Jyun Dong, Yu Hsiang Tung, Chien Chih Chen, Jyh-Jong Liao, Yii-Wen Pan - Show less +4 more 10 Jan 2011-Engineering Geology 105 Citations	The resulting high overall prediction power demonstrates the robustness of the proposed logistic regression models.

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How can logistic regression be used to improve public health outcomes?4 answersStep 1: Logistic regression can be used to predict binary outcomes, such as road traffic fatalities, and identify risk factors for diseases like prostate cancer, thus contributing to public health interventions. It has been employed to model the likelihood of fatal accidents based on sex, and to identify significant risk factors for prostate cancer, including urban residency, smoking, age, and family history. Additionally, logistic regression has been widely used in the medical field and has yielded impressive results in predicting outcomes related to public health, such as hospital readmissions and disease risk factors. Step 2: - "Context_2" - "Context_3" - "Context_1" Step 3: Logistic regression can be used to predict binary outcomes, such as road traffic fatalities, and identify risk factors for diseases like prostate cancer, thus contributing to public health interventions. It has been employed to model the likelihood of fatal accidents based on sex, and to identify significant risk factors for prostate cancer, including urban residency, smoking, age, and family history. Additionally, logistic regression has been widely used in the medical field and has yielded impressive results in predicting outcomes related to public health, such as hospital readmissions and disease risk factors.

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