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- Logistic Regression and Least Absolute Shrinkage and Selection Operator
- Hyunyong Lee, Hun-Sung Kim
- Cardiovasc Prev Pharmacother. 2020;2(4):142-146. Published online October 31, 2020
- DOI: https://doi.org/10.36011/cpp.2020.2.e15
- 13,777 View
- 71 Download
- 1 Citations
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Abstract
PDF - Logistic regression, a model that forms a binary dependent variable and one or more independent variable(s), is used especially in epidemiological studies. By understanding the logistic model and its applications, such as odds ratio (OR) and performance efficiency, the concept of logistic regression can be easily grasped. The purpose of this article is to 1) introduce logistic regression, including odds and OR, 2) present predictive efficiency, such as area under the curve, and 3) explain the caution of logistic regression analysis.
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Citations
Citations to this article as recorded by
- Perceptron: Basic Principles of Deep Neural Networks
Eung-Hee Kim, Hun-Sung Kim
Cardiovascular Prevention and Pharmacotherapy.2021; 3(3): 64. CrossRef
- Perceptron: Basic Principles of Deep Neural Networks
- From Traditional Statistical Methods to Machine and Deep Learning for Prediction Models
- Jun Hyeok Lee, Dae Ryong Kang
- Cardiovasc Prev Pharmacother. 2020;2(2):50-55. Published online April 30, 2020
- DOI: https://doi.org/10.36011/cpp.2020.2.e6
- 9,705 View
- 95 Download
- 5 Citations
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Abstract
PDF
- Traditional statistical methods have low accuracy and predictability in the analysis of large amounts of data. In this method, non-linear models cannot be developed. Moreover, methods used to analyze data for a single time point exhibit lower performance than those used to analyze data for multiple time points, and the difference in performance increases as the amount of data increases. Using deep learning, it is possible to build a model that reflects all information on repeated measures. A recurrent neural network can be built to develop a predictive model using repeated measures. However, there are long-term dependencies and vanishing gradient problems. Meanwhile, long short-term memory method can be applied to solve problems with long-term dependency and vanishing gradient by assigning a fixed weight inside the cell state. Unlike traditional statistical methods, deep learning methods allow researchers to build non-linear models with high accuracy and predictability, using information from multiple time points. However, deep learning models cannot be interpreted; although, recently, many methods have been developed to do so by weighting time points and variables using attention algorithms, such as ReversE Time AttentIoN (RETAIN). In the future, deep learning methods, as well as traditional statistical methods, will become essential methods for big data analysis.
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Citations
Citations to this article as recorded by- Longitudinal machine learning prediction of non-suicidal self-injury among Chinese adolescents: A prospective multicenter Cohort study
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Journal of Affective Disorders.2026; 392: 120110. CrossRef - BOBM: an adaptive deep learning framework for extended-window sepsis prediction with cross-institutional generalizability
Wanxuan Li, Kai Xiao, Boyuan Gu, Haoyu Zheng, Dan Shao, Ren Mo, Qiang Wu, Qiong Yang
Frontiers in Medicine.2026;[Epub] CrossRef - Statistical Approaches vs. Artificial Intelligence Models in Scientific Inquiry: What to Choose When
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European Journal of Cancer.2025; 229: 115786. CrossRef - Parkinson’s Disease Classification Using Machine Learning and Wrist Rigidity Measurements from an Active Orthosis
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Journal of Sensor and Actuator Networks.2025; 15(1): 1. CrossRef
- Longitudinal machine learning prediction of non-suicidal self-injury among Chinese adolescents: A prospective multicenter Cohort study
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