Turkiye Klinikleri Journal of Biostatistics

.: ORIGINAL RESEARCH
Yenidoğanın Ağırlığının Tahminine Yönelik Olarak Elde Edilen Klinik Verilerin Klasik ve Bulanık Doğrusal Regresyon Modelleri İle Analizi
Clinical Data Obtained for Prediction of the Weight of the Newborn Analysis with Classical and Fuzzy Linear Regression Models
Derviş TOPUZa
aNiğde Ömer Halisdemir Üniversitesi, Niğde Zübeyde Hanım Sağlık Hizmetleri Meslek Yüksekokulu, Niğde, TÜRKİYE
Turkiye Klinikleri J Biostat. 2020;12(3):320-34
doi: 10.5336/biostatic.2020-74850
Article Language: TR
Full Text
ÖZET
 Amaç: Klinik araştırmalarda bağımlı (Y) değişken ile bağımsız (X) değişkenler arası ilişki regresyon analizi ile ifade edilir. Fakat bu değişkenlerden birisi veya birden daha fazlası belirsizlik ve aykırı değerler içeriyor ise klasik regresyon analizi yapılamaz. X değişkenlerinde (bağımsız değişken) belirsiz ve aykırı gözlem değerleri olduğunda bulanık doğrusal regresyon çözümleme yaklaşımının uygulanışı tanıtılıp, bir klinik örnek üzerinde daha güvenilir tahminlerin elde edildiği tartışıldı. Gereç ve Yöntemler: Genel olarak, bulanık regresyon modellerinin analizinde kullanılan minimum bulanıklık yaklaşımının teorik temelleri ve süreçleri açıklanmıştır. Klasik ve bulanık yöntemle tahmin edilen değerler ile gözlemlenen değerler arasında istatistiksel olarak anlamlı bir fark olup olmadığını h=0.5 'bulanıklık tolerans seviyesi' değerinde hesapladık. Hesaplanan değerler arasındaki uyumluluğu gösteren uyum iyiliği test ölçüt kriterleri olarak hata kareler ortalaması ve belirtme katsayısı (R2) indexleri kullanılmıştır. Daha sonra, hesaplanan değerleri grafiksel olarak gösterip yorumladık. Bulgular: Klasik ve bulanık doğrusal regresyon analiz yöntemleri ile yenidoğanların tahmin edilen ortalama ağırlıklarına ait standart hata ve R2 değerleri sırasıyla 2635 (g) ± 32.82 (g); R_(gözlenen/Ktahmin)^2=0.61 ve 3117.72 (g) ±21.97 (g); R_(gözlenen/Btahmin)^2=0.97 olarak hesaplanmıştır. 22 iterasyonda oluşturulan bulanık doğrusal regresyon modelinin bulanıklığı 49.789 olarak hesaplanmıştır. Sonuç: Yaklaşıma göre yenidoğanın doğum ağırlığını (Yi) (g) tahmin etmede etkisi olduğu varsayılan annenin gebelikteki yaşı, doğumdaki ağırlığı, eğitim düzeyi ile oruçlu gün sayısının doğum ağırlığı üzerine etkisi önemli bulundu. Yöntemin klinik çalışmalarda da kullanılabileceği gösterilerek alternatif bir yaklaşım olabileceği önerilmiştir.

Anahtar Kelimeler: Bulanık doğrusal regresyon; bulanık katsayılar; en küçük kare tahmini; yenidoğan
ABSTRACT
 Objective: In clinical studies, the relationship between dependent (Y) variable and independent (X) variables is expressed by regression analysis. However, if one or more of these variables contains uncertainty and outliers, classical regression analysis cannot be performed. The application of the fuzzy linear regression analysis approach was introduced when there were ambiguous and outliers in X variables and it was discussed that more reliable estimates were obtained on a clinical sample. Material and Methods: We showed whether there is a statistically significant difference between observed values and estimated values calculated using fuzzy linear regression analysis methods based on classical and linear programming. In clinical studies, we suggested an h=0.5 value, which we call 'turbidity tolerance level'. We used mean squared error, square root of mean squares error and the coefficient of determination (R2) indexes as the goodness of fit test criteria showing the compatibility between the values calculated at the suggested h-level. Then, we showed and interpret the calculated values graphically. Results: With classical and fuzzy linear regression analysis methods, standard error and R2 values of the estimated average weights of newborns were calculated as 2.635 (g) ± 32.82 (g); R_(observed/Kestimated)^2=0.61 and 3.117.72 (g) ± 21.97 (g); R_(observed/Bestimated)^2=0.97, respectively. The turbidity of the fuzzy linear regression model created in 22 iterations was calculated as 49.789. Conclusion: According to the approach, the effect of the mother's age, birth weight, education level and number of fasting days on birth weight was found to be significant. It has been suggested that the method can be used in clinical studies as an alternative approach.

Keywords: Fuzzy linear regression; fuzzy coefficients; least square estimation; newborn
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