Tıp Bilişiminde Veri Madenciliği Yöntemleri Kullanılarak Hastalıkların Tahmin Edilmesitıp Bilişiminde Veri Madenciliği Yöntemleri Kullanılarak Hastalıkların Tahmin Edilmesi

Abstract

Nowadays, Data Mining is an increasingly important tool, especially in the administration of healthcare enterprises and in determining health-related policies, as it provides support for the decision-making processes of businesses by revealing information hidden from large dimensional data. Moreover, scientific publications have been made in the field of health in recent years to diagnose diseases using data mining algorithms. In this thesis, most frequently used data mining classification methods were examined, and a study was conducted to diagnose 18 different diseaes in the urology branch using the data collected from the patients who applied to the urology branch of four different public hospitals. For this purpose, classification algorithms Random Forest, Random Tree, Multilayer Perception, IBk, Kstar one of the sample based algorithms, Simple Logistic and Naive Bayes from statistical algorithms and ZeroR from rule learning algorithms were used, and the correct classification rates of the created models, namely how correctly they diagnosed the diseases, were examined. Among these algorithms Random Forest, Simple Logistic and Multilayer Perception algorithms have been found to be more successful in diagnosis than others. In future studies, an application can be developed for the diagnosis of diseases related to urology branch or diseases seen other branches by using the algorithms mentioned here. Thus, it may be possible to give healthcare professionals an idea in the diagnosis and to reduce their workload, to find the diseases in advance with early diagnosis and to shorten the treatment period.