Logistic Regression as a Classifier in Health Research

In recent years, the use of classification methods in machine learning, which is very popular among artificial intelligence methods, has been increasing due to the increase in the availability of health data. There are several classifier algorithms or methods in both supervised and unsupervised machine learning algorithms. Major unsupervised learning methods can be listed as cluster analysis and principal component analysis. Some notable examples of supervised machine learning algorithms are logistic regression, discriminant analysis, decision trees, nearest neighbor, neural network, naive Bayes, random forest, and support vector machine. In this chapter, binary logistic regression, one of the classification techniques in artificial intelligence, supervised machine learning, and econometrics, is theoretically discussed. In addition, the place and importance of this method in empirical applications in the field of health are briefly mentioned. In this section, an experimental study has also been conducted using the logit model to classify patients living in Adana province in Turkey according to their hospital services preferences. The data used in the study were collected by surveying in Adana. In the study, a binary logit model was used to classify patients and investigate the effects of many factors that may affect patient classification. Also, many tests have been conducted to investigate the classification ability of the model. As a result, the test results show that the model has good performance.

Keywords: Artificial Intelligence, Binary Response, Classifier in Health Research, Consumer Preference, Econometrics, Healthcare, Hospital Preference, Logistic Function, Logistic Regression, Machine Learning, Marginal Effects, Maximum Likelihood Estimator, Microdata, Odds Ratio, Pooled Data, Private Hospital Choice, Sigmoid Function, Statistics, Supervised Machine Learning, Unsupervised Machine Learning.