Feedforward Artificial Neural Network to estimate IQ of mental retarded people from different psychometric instruments

Alessandro G. Di Nuovo, Santo Di Nuovo, Serafino Buono, Vincenzo Catania

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Citation (Scopus)

Abstract

The estimation of a person's Intelligence Quotient (IQ) by means of psychometric tests is indispensable in order to determine possible mental retardation or intellectual disability based on the most common classification systems. With some subjects, however, it is not possible to use more complex tools such as the Wechsler scales, which are universally recognized as being the most reliable, in that they require minimum capabilities which are not always possessed by people affected by serious cognitive defects. This means it is necessary to use other psychodiagnostic tools that are better suited to a subject's specific condition, but also that it is then necessary to reach a common metric so as to compare the reliability of the results obtained and thus ensure a homogeneous diagnosis. The concrete problem arising in the diagnosis of mental retardation using the IQ is thus the need to match the scores, obtained using different tests, with the Wechsler IQ, which is the most commonly used and universally recognized test for the diagnosis of degrees of retardation. In this paper we present the use of feedforward Artificial Neural Networks (ANNs) to search for the best estimate of the Wechsler IQ provided by four different psychodiagnostic tools. To this end, a database was created, administering four different tests, besides the Wechsler scale, to the same group of mentally retarded subjects, in order to generate IQ estimation models on the basis of the scores obtained in the other tests via the use of ANNs. The results were then compared with other statistic modeling methods, in terms of accuracy and reliability.

Original languageEnglish
Title of host publicationProceedings of the International Joint Conference on Neural Networks
Pages690-696
Number of pages7
DOIs
Publication statusPublished - 2009
Event2009 International Joint Conference on Neural Networks, IJCNN 2009 - Atlanta, GA, United States
Duration: Jun 14 2009Jun 19 2009

Other

Other2009 International Joint Conference on Neural Networks, IJCNN 2009
CountryUnited States
CityAtlanta, GA
Period6/14/096/19/09

Fingerprint

Neural networks
Statistics
Defects

ASJC Scopus subject areas

  • Software
  • Artificial Intelligence

Cite this

Di Nuovo, A. G., Di Nuovo, S., Buono, S., & Catania, V. (2009). Feedforward Artificial Neural Network to estimate IQ of mental retarded people from different psychometric instruments. In Proceedings of the International Joint Conference on Neural Networks (pp. 690-696). [5178847] https://doi.org/10.1109/IJCNN.2009.5178847

Feedforward Artificial Neural Network to estimate IQ of mental retarded people from different psychometric instruments. / Di Nuovo, Alessandro G.; Di Nuovo, Santo; Buono, Serafino; Catania, Vincenzo.

Proceedings of the International Joint Conference on Neural Networks. 2009. p. 690-696 5178847.

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Di Nuovo, AG, Di Nuovo, S, Buono, S & Catania, V 2009, Feedforward Artificial Neural Network to estimate IQ of mental retarded people from different psychometric instruments. in Proceedings of the International Joint Conference on Neural Networks., 5178847, pp. 690-696, 2009 International Joint Conference on Neural Networks, IJCNN 2009, Atlanta, GA, United States, 6/14/09. https://doi.org/10.1109/IJCNN.2009.5178847
Di Nuovo AG, Di Nuovo S, Buono S, Catania V. Feedforward Artificial Neural Network to estimate IQ of mental retarded people from different psychometric instruments. In Proceedings of the International Joint Conference on Neural Networks. 2009. p. 690-696. 5178847 https://doi.org/10.1109/IJCNN.2009.5178847
Di Nuovo, Alessandro G. ; Di Nuovo, Santo ; Buono, Serafino ; Catania, Vincenzo. / Feedforward Artificial Neural Network to estimate IQ of mental retarded people from different psychometric instruments. Proceedings of the International Joint Conference on Neural Networks. 2009. pp. 690-696
@inproceedings{fc6fb5b199a142979c563ad9c55184fd,
title = "Feedforward Artificial Neural Network to estimate IQ of mental retarded people from different psychometric instruments",
abstract = "The estimation of a person's Intelligence Quotient (IQ) by means of psychometric tests is indispensable in order to determine possible mental retardation or intellectual disability based on the most common classification systems. With some subjects, however, it is not possible to use more complex tools such as the Wechsler scales, which are universally recognized as being the most reliable, in that they require minimum capabilities which are not always possessed by people affected by serious cognitive defects. This means it is necessary to use other psychodiagnostic tools that are better suited to a subject's specific condition, but also that it is then necessary to reach a common metric so as to compare the reliability of the results obtained and thus ensure a homogeneous diagnosis. The concrete problem arising in the diagnosis of mental retardation using the IQ is thus the need to match the scores, obtained using different tests, with the Wechsler IQ, which is the most commonly used and universally recognized test for the diagnosis of degrees of retardation. In this paper we present the use of feedforward Artificial Neural Networks (ANNs) to search for the best estimate of the Wechsler IQ provided by four different psychodiagnostic tools. To this end, a database was created, administering four different tests, besides the Wechsler scale, to the same group of mentally retarded subjects, in order to generate IQ estimation models on the basis of the scores obtained in the other tests via the use of ANNs. The results were then compared with other statistic modeling methods, in terms of accuracy and reliability.",
author = "{Di Nuovo}, {Alessandro G.} and {Di Nuovo}, Santo and Serafino Buono and Vincenzo Catania",
year = "2009",
doi = "10.1109/IJCNN.2009.5178847",
language = "English",
isbn = "9781424435531",
pages = "690--696",
booktitle = "Proceedings of the International Joint Conference on Neural Networks",

}

TY - GEN

T1 - Feedforward Artificial Neural Network to estimate IQ of mental retarded people from different psychometric instruments

AU - Di Nuovo, Alessandro G.

AU - Di Nuovo, Santo

AU - Buono, Serafino

AU - Catania, Vincenzo

PY - 2009

Y1 - 2009

N2 - The estimation of a person's Intelligence Quotient (IQ) by means of psychometric tests is indispensable in order to determine possible mental retardation or intellectual disability based on the most common classification systems. With some subjects, however, it is not possible to use more complex tools such as the Wechsler scales, which are universally recognized as being the most reliable, in that they require minimum capabilities which are not always possessed by people affected by serious cognitive defects. This means it is necessary to use other psychodiagnostic tools that are better suited to a subject's specific condition, but also that it is then necessary to reach a common metric so as to compare the reliability of the results obtained and thus ensure a homogeneous diagnosis. The concrete problem arising in the diagnosis of mental retardation using the IQ is thus the need to match the scores, obtained using different tests, with the Wechsler IQ, which is the most commonly used and universally recognized test for the diagnosis of degrees of retardation. In this paper we present the use of feedforward Artificial Neural Networks (ANNs) to search for the best estimate of the Wechsler IQ provided by four different psychodiagnostic tools. To this end, a database was created, administering four different tests, besides the Wechsler scale, to the same group of mentally retarded subjects, in order to generate IQ estimation models on the basis of the scores obtained in the other tests via the use of ANNs. The results were then compared with other statistic modeling methods, in terms of accuracy and reliability.

AB - The estimation of a person's Intelligence Quotient (IQ) by means of psychometric tests is indispensable in order to determine possible mental retardation or intellectual disability based on the most common classification systems. With some subjects, however, it is not possible to use more complex tools such as the Wechsler scales, which are universally recognized as being the most reliable, in that they require minimum capabilities which are not always possessed by people affected by serious cognitive defects. This means it is necessary to use other psychodiagnostic tools that are better suited to a subject's specific condition, but also that it is then necessary to reach a common metric so as to compare the reliability of the results obtained and thus ensure a homogeneous diagnosis. The concrete problem arising in the diagnosis of mental retardation using the IQ is thus the need to match the scores, obtained using different tests, with the Wechsler IQ, which is the most commonly used and universally recognized test for the diagnosis of degrees of retardation. In this paper we present the use of feedforward Artificial Neural Networks (ANNs) to search for the best estimate of the Wechsler IQ provided by four different psychodiagnostic tools. To this end, a database was created, administering four different tests, besides the Wechsler scale, to the same group of mentally retarded subjects, in order to generate IQ estimation models on the basis of the scores obtained in the other tests via the use of ANNs. The results were then compared with other statistic modeling methods, in terms of accuracy and reliability.

UR - http://www.scopus.com/inward/record.url?scp=70449581221&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=70449581221&partnerID=8YFLogxK

U2 - 10.1109/IJCNN.2009.5178847

DO - 10.1109/IJCNN.2009.5178847

M3 - Conference contribution

AN - SCOPUS:70449581221

SN - 9781424435531

SP - 690

EP - 696

BT - Proceedings of the International Joint Conference on Neural Networks

ER -