Some machine learning techniques for medical diagnosis

There is a huge increase in the amount of biological data obtained with the great breakthroughs in technology, and the amount of this data increases exponentially every day. The use of data mining algorithms and machine learning methods has become widespread in the field of health, because analysis and interpretation of biological data is very difficult with traditional methods. In this study, biochemistry and hormone values are used to make some prediction on medical diagnosis with some machine learning methods such as linear regression, generalized linear regression, deep learning, random forests, gradient boosted trees and support vector machines. With the analysis of the laboratory results according to the reference ranges, it is tried to reveal some new relations of biochemistry and hormone parameters. Finally, the results are presented and the performance of the model is evaluated.