Hipertansiyonun Tahmini için Çoklu Tahmin Modellerinin Karşılaştırılması

Abstract

Bu çalışmada, kontrol ve hipertansiyonlu hasta grubunun tahmini için lojistik regresyon analizi (LR), flexible diskriminant analizi (FDA) ve neural networks (NNs) karşılaştırıldı. Aile hikayesi, lipoprotein A, trigliserid, sigara kullanımı ve vücut kitle indeksi tahminleyici değişken olarak ele alındı. Veriler, 2001 yılında Trakya Üniversitesi Tıp Fakültesi Kardiyoloji Kliniğinden elde edildi. Bütün modellerin ROC eğrisi altındaki alanları, 0.793-0.984 aralığında yer aldı. NNs 'nin duyarlılık, özgüllük ve doğruluk oranlan %90 'dan daha yüksek bulundu. NNs ve LR ile NNs ve FDA 'nın ROC eğrisi altında kalan alanları, istatistiksel olarak farklı bulundu (sırasıyla p<0.0005 ve p<0.0005). FDA ve LR'nin ROC eğrisi altında kalan alanları istatistiksel olarak farklı bulunmadı (p=O.394). NNs 'nin performansının LR ile FDA 'dan istatistiksel olarak daha iyi olduğuna karar verildi.

Keywords

• Lojistik Regresyon Analizi
• Neural Networks
• Flexible Diskriminant Analizi
• ROC Eğrisi

Comparison of Multiple Prediction Models for Hypertension

Abstract

In this study, we compared logistic regression analysis (LR), flexible discriminant analysis (FDA) and neural networks (NNs) for predict of control and hypertension groups. Predictor variables were family history, lipoprotein A, triglyceride, smoking and body mass index. The data were collected from Cardiology Clinic of Trakya University Medical Faculty in Turkey, 2001. All models had areas under the receiver operating characteristic curve (ROC) in the 0.793-0.984 range. NNs had sensitivity, specificity, and accuracy greater than 90% at ideal threshold. ROC curve areas of NNs and LR, and NNs and FDA were statistically different (p<0.0005 and p<0.0005 respectively). ROC curve areas of FDA and LR were not statistically different (p=0.394). We concluded that performance of NNs was statistically better than LR and FDA.

Keywords

• Neural Networks
• Flexible Discriminant Analysis
• Logistic Regression
• ROC Curve

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