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Evaluating the Risk of Metabolic Syndrome Based on an Artificial Intelligence Model
المؤلفون المشاركون
Chen, Hui
Xiong, Shenghua
Ren, Xuan
المصدر
العدد
المجلد 2014، العدد 2014 (31 ديسمبر/كانون الأول 2014)، ص ص. 1-12، 12ص.
الناشر
Hindawi Publishing Corporation
تاريخ النشر
2014-05-05
دولة النشر
مصر
عدد الصفحات
12
التخصصات الرئيسية
الملخص EN
Metabolic syndrome is worldwide public health problem and is a serious threat to people's health and lives.
Understanding the relationship between metabolic syndrome and the physical symptoms is a difficult and challenging task, and few studies have been performed in this field.
It is important to classify adults who are at high risk of metabolic syndrome without having to use a biochemical index and, likewise, it is important to develop technology that has a high economic rate of return to simplify the complexity of this detection.
In this paper, an artificial intelligence model was developed to identify adults at risk of metabolic syndrome based on physical signs; this artificial intelligence model achieved more powerful capacity for classification compared to the PCLR (principal component logistic regression) model.
A case study was performed based on the physical signs data, without using a biochemical index, that was collected from the staff of Lanzhou Grid Company in Gansu province of China.
The results show that the developed artificial intelligence model is an effective classification system for identifying individuals at high risk of metabolic syndrome.
نمط استشهاد جمعية علماء النفس الأمريكية (APA)
Chen, Hui& Xiong, Shenghua& Ren, Xuan. 2014. Evaluating the Risk of Metabolic Syndrome Based on an Artificial Intelligence Model. Abstract and Applied Analysis،Vol. 2014, no. 2014, pp.1-12.
https://search.emarefa.net/detail/BIM-1013473
نمط استشهاد الجمعية الأمريكية للغات الحديثة (MLA)
Chen, Hui…[et al.]. Evaluating the Risk of Metabolic Syndrome Based on an Artificial Intelligence Model. Abstract and Applied Analysis No. 2014 (2014), pp.1-12.
https://search.emarefa.net/detail/BIM-1013473
نمط استشهاد الجمعية الطبية الأمريكية (AMA)
Chen, Hui& Xiong, Shenghua& Ren, Xuan. Evaluating the Risk of Metabolic Syndrome Based on an Artificial Intelligence Model. Abstract and Applied Analysis. 2014. Vol. 2014, no. 2014, pp.1-12.
https://search.emarefa.net/detail/BIM-1013473
نوع البيانات
مقالات
لغة النص
الإنجليزية
الملاحظات
Includes bibliographical references
رقم السجل
BIM-1013473
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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تقوم هذه الخدمة بالتحقق من التشابه أو الانتحال في الأبحاث والمقالات العلمية والأطروحات الجامعية والكتب والأبحاث باللغة العربية، وتحديد درجة التشابه أو أصالة الأعمال البحثية وحماية ملكيتها الفكرية. تعرف اكثر
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