Abstract
In today’s scenarios many healthcare decisions are being taken by predictive modeling and machine learning techniques. With this review, we focused on logistic regression model, a kind of predictive modeling used in machine learning, and how healthcare researchers take decisions by the help of predictive modeling. For a better data analysis in healthcare, we need to understand the concept of logistic regression as well as others terms, which are linked with it. so that we can clearly understand the concept behind it and implement in medical research. In this review we worked on an example and illustrated how to perform logistic regression using R programming language. The aim of this paper is to understand logistic regression in healthcare and implement it for decision making.
Subject
Public Health, Environmental and Occupational Health,Medicine (miscellaneous),Epidemiology
Reference18 articles.
1. Oommen, T., Baise, L.G. and Vogel, R.M. (2011) Sampling Bias and Class Imbalance in Maximum-Likelihood Logistic Regres-sion. Mathematical Geosciences, 43, 99-120. https://doi.org/10.1007/s11004-010-9311-8
2. Cramer, J.S. (2002) The Origins of Logistic Regression. Tin-bergen Institute Working Paper.
3. Tu, J.V. (1996) Advantages and Disadvantages of Using Artifi-cial Neural Networks versus Logistic Regression for Predict-ing Medical Outcomes. Journal of Clinical Epidemiology, 49, 1225-1231. https://doi.org/10.1016/S0895-4356(96)00002-9
4. Hosmer D.W. and Lemeshow, S. (2000) Applied Logistic Re-gression. 2nd Edition, Wiley, New York. https://doi.org/10.1002/0471722146 https://onlinelibrary.wiley.com/doi/book/10.1002/0471722146
5. King, G. and Zeng, L. (2001) Logistic Regression in Rare Events Data. Political Analysis, 9, 137-163. https://doi.org/10.1093/oxfordjournals.pan.a004868
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