دورية أكاديمية

Integrating Feature Ranking with Ensemble Learning and Logistic Model Trees for the Prediction of Postprandial Blood Glucose Elevation

التفاصيل البيبلوغرافية
العنوان: Integrating Feature Ranking with Ensemble Learning and Logistic Model Trees for the Prediction of Postprandial Blood Glucose Elevation
المؤلفون: Chen,Jason, Kang,Hsiao-Yen, Wang,Mei-Chin
المصدر: JUCS - Journal of Universal Computer Science 24(6): 797-812
بيانات النشر: Journal of Universal Computer Science
سنة النشر: 2018
المجموعة: Pensoft Publishers
مصطلحات موضوعية: postprandial blood glucose elevation, cohort dataset, data mining, chronic diseases
الوصف: Postprandial blood glucose (PBG) elevation has been documented as a significant development of diabetes and cardiovascular diseases. Surprisingly, few studies have provided an effective model for predicting PBG elevation. This work presents the classification of PBG in a cohort study via integrating feature ranking with ensemble learning and logistic model trees. We used a cohort dataset that included 1,438 individuals from Landseed Hospital in Taiwan. Data from 2006 to 2013 were collected. To evaluate the performance of the proposed model, four well-known data mining classifiers (Naive Bayes tree algorithm, alternating decision tree, radial basis functions neural network, and Adaboost.M1) were employed in this study. The proposed model provided a reasonably accurate classification for predicting the PBG levels. Twenty-seven risk factors were identified as important risk factors for PBG elevation. The role of PBG should be emphasized and not that of PBG elevation. The predictive factors of PBG must be related to the development of certain diseases.
نوع الوثيقة: article in journal/newspaper
وصف الملف: text/html
اللغة: English
العلاقة: info:eu-repo/semantics/altIdentifier/eissn/0948-6968; info:eu-repo/semantics/altIdentifier/pissn/0948-695X
DOI: 10.3217/jucs-024-06-0797
الإتاحة: https://doi.org/10.3217/jucs-024-06-0797Test
https://lib.jucs.org/article/23307Test/
حقوق: info:eu-repo/semantics/openAccess
رقم الانضمام: edsbas.802CA3B9
قاعدة البيانات: BASE
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