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.
