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Effect of Missing Data Imputation on Deep Learning Prediction Performance for Vesicoureteral Reflux and Recurrent Urinary Tract Infection Clinical Study
المؤلفون المشاركون
Köse, Timur
Özgür, Su
Coşgun, Erdal
Keskinoğlu, Ahmet
Keskinoğlu, Pembe
المصدر
العدد
المجلد 2020، العدد 2020 (31 ديسمبر/كانون الأول 2020)، ص ص. 1-15، 15ص.
الناشر
Hindawi Publishing Corporation
تاريخ النشر
2020-07-15
دولة النشر
مصر
عدد الصفحات
15
التخصصات الرئيسية
الملخص EN
Missing observations are always a challenging problem that we have to deal with in diseases that require follow-up.
In hospital records for vesicoureteral reflux (VUR) and recurrent urinary tract infection (rUTI), the number of complete cases is very low on demographic and clinical characteristics, laboratory findings, and imaging data.
On the other hand, deep learning (DL) approaches can be used for highly missing observation scenarios with its own missing ratio algorithm.
In this study, the effects of multiple imputation techniques MICE and FAMD on the performance of DL in the differential diagnosis were compared.
The data of a retrospective cross-sectional study including 611 pediatric patients were evaluated (425 with VUR, 186 with rUTI, 26.65% missing ratio) in this research.
CNTK and R 3.6.3 have been used for evaluating different models for 34 features (physical, laboratory, and imaging findings).
In the differential diagnosis of VUR and rUTI, the best performance was obtained by deep learning with MICE algorithm with its values, respectively, 64.05% accuracy, 64.59% sensitivity, and 62.62% specificity.
FAMD algorithm performed with accuracy=61.52, sensitivity=60.20, and specificity was found out to be 61.00 with 3 principal components on missing imputation phase.
DL-based approaches can evaluate datasets without doing preomit/impute missing values from datasets.
Once DL method is used together with appropriate missing imputation techniques, it shows higher predictive performance.
نمط استشهاد جمعية علماء النفس الأمريكية (APA)
Köse, Timur& Özgür, Su& Coşgun, Erdal& Keskinoğlu, Ahmet& Keskinoğlu, Pembe. 2020. Effect of Missing Data Imputation on Deep Learning Prediction Performance for Vesicoureteral Reflux and Recurrent Urinary Tract Infection Clinical Study. BioMed Research International،Vol. 2020, no. 2020, pp.1-15.
https://search.emarefa.net/detail/BIM-1132123
نمط استشهاد الجمعية الأمريكية للغات الحديثة (MLA)
Köse, Timur…[et al.]. Effect of Missing Data Imputation on Deep Learning Prediction Performance for Vesicoureteral Reflux and Recurrent Urinary Tract Infection Clinical Study. BioMed Research International No. 2020 (2020), pp.1-15.
https://search.emarefa.net/detail/BIM-1132123
نمط استشهاد الجمعية الطبية الأمريكية (AMA)
Köse, Timur& Özgür, Su& Coşgun, Erdal& Keskinoğlu, Ahmet& Keskinoğlu, Pembe. Effect of Missing Data Imputation on Deep Learning Prediction Performance for Vesicoureteral Reflux and Recurrent Urinary Tract Infection Clinical Study. BioMed Research International. 2020. Vol. 2020, no. 2020, pp.1-15.
https://search.emarefa.net/detail/BIM-1132123
نوع البيانات
مقالات
لغة النص
الإنجليزية
الملاحظات
Includes bibliographical references
رقم السجل
BIM-1132123
قاعدة معامل التأثير والاستشهادات المرجعية العربي "ارسيف Arcif"
أضخم قاعدة بيانات عربية للاستشهادات المرجعية للمجلات العلمية المحكمة الصادرة في العالم العربي
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