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Acta Medica Iranica، جلد ۵۱، شماره ۶، صفحات ۳۴۷-۳۵۲
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عنوان انگلیسی Predicting developmental disorder in infants using an artificial neural network.
چکیده انگلیسی مقاله Early recognition of developmental disorders is an important goal, and equally important is avoiding misdiagnosing a disorder in a healthy child without pathology. The aim of the present study was to develop an artificial neural network using perinatal information to predict developmental disorder at infancy. A total of 1,232 mother-child dyads were recruited from 6,150 in the original data of Karaj, Alborz Province, Iran. Thousands of variables are examined in this data including basic characteristics, medical history, and variables related to infants. The validated Infant Neurological International Battery test was employed to assess the infant's development. The concordance indexes showed that true prediction of developmental disorder in the artificial neural network model, compared to the logistic regression model, was 83.1% vs. 79.5% and the area under ROC curves, calculated from testing data, were 0.79 and 0.68, respectively. In addition, specificity and sensitivity of the ANN model vs. LR model was calculated 93.2% vs. 92.7% and 39.1% vs. 21.7%. An artificial neural network performed significantly better than a logistic regression model.
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نویسندگان مقاله دکتر فرین سلیمانی | farin soleimani
pediatric neurorehabilitation research center, university of social welfare amp;amp; rehabilitation sciences,tehran, iran.


رباب تیموری | robab teymouri
pediatric neurorehabilitation research center, university of social welfare amp;amp; rehabilitation sciences,tehran, iran.


اکبر بیگلریان | akbar biglarian
department of biostatistics, university of social welfare and rehabilitation sciences tehran, iran.

سازمان اصلی تایید شده: دانشگاه علوم بهزیستی و توانبخشی (University of social welfare and rehabilitation sciences)

نشانی اینترنتی http://acta.tums.ac.ir/index.php/acta/article/view/4432
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زبان مقاله منتشر شده en
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