University of Mohaghegh Ardabili Journal of Learning Disabilities 2251-8673 9 4 2020 08 22 Designing a dynamic neuro-cognitive screener with the optimal intelligent algorithm: Predicting learning disabilities and other common neuro-developmental disorders Designing a dynamic neuro-cognitive screener with the optimal intelligent algorithm: Predicting learning disabilities and other common neuro-developmental disorders 7 33 952 10.22098/jld.2020.952 FA M Delavarian PhD in Psychology, Post doc in Neuropsychology Gh Afrooz Distinguished Professor in Exceptional Child psychology, Tehran university Journal Article 2020 10 06 The aim of this research was developing an optimal dynamic neuro-cognitive screener for prediction of pre-schoolers at risk of common neurodevelopmental disorders. This research is a kind of assessment and diagnostic research, according to its data collection method. The computerized neuro-cognitive program designed by Delavarian et al. was applied for data collection. The selected preschoolers, with cluster random sampling, executed the program and the results were saved and followed for two years, till the definite diagnosis was achieved. These data were applied in designing two neuro-cognitive screeners. In comparison of the networks, the intelligent radial basis functions screener was selected with the more accuracy, sensitivity and specificity (91.41%, 93.65%, and 96.01%, respectively), in screening children at risk of the common neurodevelopmental disorders. Therefore, this designed neuro-cognitive screener could be confidentially applied in early diagnosis of preschoolers at risk of the mentioned disorders. The aim of this research was developing an optimal dynamic neuro-cognitive screener for prediction of pre-schoolers at risk of common neurodevelopmental disorders. This research is a kind of assessment and diagnostic research, according to its data collection method. The computerized neuro-cognitive program designed by Delavarian et al. was applied for data collection. The selected preschoolers, with cluster random sampling, executed the program and the results were saved and followed for two years, till the definite diagnosis was achieved. These data were applied in designing two neuro-cognitive screeners. In comparison of the networks, the intelligent radial basis functions screener was selected with the more accuracy, sensitivity and specificity (91.41%, 93.65%, and 96.01%, respectively), in screening children at risk of the common neurodevelopmental disorders. Therefore, this designed neuro-cognitive screener could be confidentially applied in early diagnosis of preschoolers at risk of the mentioned disorders. https://jld.uma.ac.ir/article_952_b7ac76fe3b45ad7023d8d01a175a7a37.pdf