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Estimate of the homa-ir cut-off value for identifying subjects at risk of insulin resistance using a machine learning approach
المؤلفون المشاركون
Alya al-Ansari
Zadjali, Fahd
Abd al-Salam, Abd al-Hamid
Zidoum, Hamzah
Hedjam, Rashid
Bayoumi, Riad
al-Yahyai, Said
al-Barwani, Sulayma
المصدر
Sultan Qaboos University Medical Journal
العدد
المجلد 21، العدد 4 (30 نوفمبر/تشرين الثاني 2021)، ص ص. 604-612، 9ص.
الناشر
جامعة السلطان قابوس كلية الطب و العلوم الصحية
تاريخ النشر
2021-11-30
دولة النشر
سلطنة عمان
عدد الصفحات
9
التخصصات الرئيسية
الملخص EN
-This study describes an unsupervised machine learning approach used to estimate the homeostatic model assessment-insulin resistance (HOMA-IR) cut-off for identifying subjects at risk of IR in a given ethnic group based on the clinical data of a representative sample.
Methods: The approach was applied to analyse the clinical data of individuals with Arab ancestors, which was obtained from a family study conducted in Nizwa, Oman, between January 2000 and December 2004.
First, HOMA-IR-correlated variables were identified to which a clustering algorithm was applied.
Two clusters having the smallest overlap in their HOMA-IR values were retrieved.
These clusters represented the samples of two populations, which are insulin-sensitive subjects and individuals at risk of IR.
The cut-off value was estimated from intersections of the Gaussian functions, thereby modelling the HOMA-IR distributions of these populations.
Results: A HOMA-IR cut-off value of 1.62 ± 0.06 was identified.
The validity of this cut-off was demonstrated by showing the following: 1) that the clinical characteristics of the identified groups matched the published research findings regarding IR; 2) that a strong relationship exists between the segmentations resulting from the proposed cut-off and those resulting from the two-hour glucose cut-off recommended by the World Health Organization for detecting prediabetes.
Finally, the method was also able to identify the cut-off values for similar problems (e.g.
fasting sugar cut-off for prediabetes).
Conclusion: The proposed method defines a HOMA-IR cut-off value for detecting individuals at risk of IR.
Such methods can identify high-risk individuals at an early stage, which may prevent or delay the onset of chronic diseases such as type 2 diabetes.
نمط استشهاد جمعية علماء النفس الأمريكية (APA)
Alya al-Ansari& Bayoumi, Riad; Bayoumi, Riad& al-Yahyai, Said& Hasan, Muhammad Usamah& al-Barwani, Sulayma…[et al.]. 2021. Estimate of the homa-ir cut-off value for identifying subjects at risk of insulin resistance using a machine learning approach. Sultan Qaboos University Medical Journal،Vol. 21, no. 4, pp.604-612.
https://search.emarefa.net/detail/BIM-1366415
نمط استشهاد الجمعية الأمريكية للغات الحديثة (MLA)
Hasan, Muhammad Usamah…[et al.]. Estimate of the homa-ir cut-off value for identifying subjects at risk of insulin resistance using a machine learning approach. Sultan Qaboos University Medical Journal Vol. 21, no. 4 (Nov. 2021), pp.604-612.
https://search.emarefa.net/detail/BIM-1366415
نمط استشهاد الجمعية الطبية الأمريكية (AMA)
Alya al-Ansari& Bayoumi, Riad; Bayoumi, Riad& al-Yahyai, Said& Hasan, Muhammad Usamah& al-Barwani, Sulayma…[et al.]. Estimate of the homa-ir cut-off value for identifying subjects at risk of insulin resistance using a machine learning approach. Sultan Qaboos University Medical Journal. 2021. Vol. 21, no. 4, pp.604-612.
https://search.emarefa.net/detail/BIM-1366415
نوع البيانات
مقالات
لغة النص
الإنجليزية
الملاحظات
رقم السجل
BIM-1366415
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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