HYPOTHYROIDI AND HYPERTHYROIDI DETECTION FROM THYROID HORMONE PARAMETERS BY USING DECISION TREES

Year 2007, Volume: 5 Issue: 2, 163 - 169, 30.03.2007

Şengül Doğan İbrahim Türkoğlu

Abstract

In this study a decision support system has been
projected from the biochemistry blood parameters which will be very helpful and
will make everything easier for the physicians in the diagnosis of
Hyperthyroidi and Hypothyroidi. Based on pattern recognition process, the
system operation is achieved via the decision trees structure which is related
as one of the data mining techniques. The basic characteristic of the thyroid
hormone parameters that is, TSH, FT3, FT4, TT3 and TT4 parameters are used in
the process of entering the system and finally Hyper(+),
Hypo(+) and (-) results have been evaluated at the end of this process. Data of
120 patients are evaluated in the projected system. The results of the decision
support system have completely matched with those of the physicians’ decisions.

Keywords

Pattern Recognition, Data Mining, Decision Trees, Tyhroid Hormones, Expert System

KARAR AĞACI YÖNTEMİNİ KULLANARAK TİROİD HORMON PARAMETRELERİNDEN HİPERTİROİDİ VE HİPOTİROİDİ TEŞHİSİ

Abstract

Bu
çalışmada, biyokimya test sonuçlarından Hipertiroidi ve Hipotiroidi teşhisinde,
hekime yardımcı olacak ve kolaylık sağlayacak bir karar destek sistemi
tasarlanmıştır. Örüntü tanıma süreci esas alınmış olup, siste-min işleyişi veri
madenciliği tekniklerinden olan karar ağaçları yapısı ile sağlanmaktadır.
Sisteme giriş olarak, tiroid hormonlarından temel belirleyiciler olan TSH, FT3,
FT4, TT3, TT4 parametreleri kullanılarak, çıkış olarak ta Hiper(+), Hipo(+) ve
(-) değerlendirmelerin de bulunulmuştur. Tasarlanan sistemde 120 hasta verisi
değerlendirilmiştir. Karar destek sisteminin sonuçları, doktorun verdiği
kararlarla tamamen örtüşmüş-tür.

Keywords

Örüntü Tanıma, Veri Madenciliği, Karar Ağacı, Tiroid Hormonları, Uzman Sistemler

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