A Deep Learning Based Prediction Model for Diagnosing Diseases with Similar Symptoms

Abstract

Diagnosis of diseases with similar symptoms may cause medical errors depending on the transfer of patient complaints. In this study, diseases that are similar to each other in terms of symptoms are primarily examined. In conducted experiments Diabetis Mellitus was the focus of the study and most similar disaeses to Diabetis Mellitus were determined by using statistical data and deep learning methods. Within the scope of the study, a data set containing the symptoms of patients with this disease was created. In experiments using the data of 205 patients, it was seen that the deep learning model produced the same diagnosis with physicians with a rate of over 84% For nearly 10% of the patients used in the experiment, it was concluded that an alternative disease should also be checked.