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Obezite insan sagligini olumsuz etkileyecek duzeyde yag miktarinin vucuttaki artisi olarak tanimlanmaktadir. Yetiskin erkeklerde vucut agirliginin %15-18, kadinlarda %20-25’i yag dokusundan olusmaktadir. Bu degerlerin erkeklerde %25, kadinlarda %30 uzerine cikmasi “obezite” olarak ifade edilmektedir. Farkli bir yasam tarzi veya yasam alanina geciste bireylerin sosyal aliskanliklari gibi beslenme aliskanliklari da degismektedir. Universite ogrencilerinin farkli yasam tarzina geciste beslenme aliskanliklarinin da degismesi kendilerini obezite ile karsi karsiya birakmaktadir. Duzensiz beslenme, duzensiz uyku ve fiziksel aktivite eksikligi obezitenin ilerleyisini kolaylastirmaktadir. Bu calismada, veri madenciligi teknikleri ile universite ogrencilerinin beslenmelerine ne derece dikkat ettigi, beslenme aliskanliklari, beslenme sekilleri ve buna bagli olarak obeziteye yakalanma durumlari incelenecektir. Calismada 100 kisiye ait veri kullanilarak obeziteye yakalanma riskini ortaya cikaracak karar agaci olusturulmustur. Algoritma ile olusturulan karar agacinda amaca uygun budama islemleri gerceklestirilmistir. Karar agaci obezite teshisinde %100 basariya ulasmistir. Karar destek sistemi ile hekimlerin klinik ortamda teshis surecini hizlandirmak amaclanmistir. Obesity is defined as the increasement of the fat amount in the body to such extent that it affects the health adversely. 15-18% of the bodyweight of an adult male is made up of adipose tissues while this rate ranges between 20 to 25 % for adult females. When this rate goes over 25% in males and 30% in females, it's called obesity. Not only social habits but also dietary habits change when a person changes his or her way of life or moves to a different living space as in the case of university students who are often faced by obesity as they change their lifestyles and nutritional habits. Malnutrition, irregular sleep and lack of physical activity accelerate obesity's rise. In this study, the extent to which university students care about the nutrition, dietary habits and the obesity risk related to those factors will be probed with the help of data mining techniques. A decision tree was created to reveal the possibility of being obese based on the data obtained from 100 individuals. Pruning was done for this purpose on the decision tree which was created with an algorithm. The decision tree yielded 100% of success rate for the diagnosis of obesity. The process of the diagnosis for clinical purposes can be accelerated with the use of decision support system.

celal bayar üniversitesi fen bilimleri dergisi

Üniversite Öğrencilerinde Obeziteye Etki Eden Beslenme Davranışlarının Belirlenmesine Yönelik Karar Ağacı Uygulaması / A Decision Tree Application for the Detection of the Dietary Habits of the University Students Affecting Obesity