Recent applications and perspectives of logistic regression modelling in healthcare

The logistic regression model plays an important role in modeling and prediction in dealing with binary classification problems. In medical liao, the prediction, diagnosis, and treatment of diseases have always been a research direction that has attracted much attention. This paper analyses the latest applications of logistic regression models in the healthcare field from the aspects of disease risk prediction, diagnosis, and treatment outcome assessment, comparing and analyzing the latest applications and development prospects of logistic regression models in the field of healthcare, with the literature spanning 2018-2024. The paper also identifies the problems at the present stage and searches for future solutions to optimize them. Constructing logistic regression models can be effective in predicting the likelihood of disease occurrence, however, there are challenges in dealing with feature correlation and sample imbalance, as well as difficulties in dealing with missing values and outliers, and difficulties in fitting non-linear relationships, as well as difficulties in determining causality and sample imbalance. The study suggests that future research can focus on optimizing the exhibition of the calculated relapse model, including improving the feature selection method and parameter optimization algorithm of the model, or combining other algorithms and validation methods to improve the accuracy and stability of the model, and constructing a more comprehensive and diversified model to ensure the authenticity and accuracy of the data, to improve the generalization ability and the scope of application of the model.