Feasibility study on rapid screening of learning disabilities by number cancellation test in combination with machine learning algorithm

WANG Hongan; YU Dongchuan; LIU Fulin; CHI Xia

doi:10.11852/zgetbjzz2022-0691

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Chinese Journal of Child Health Care ›› 2023, Vol. 31 ›› Issue (6) : 595-600. DOI: 10.11852/zgetbjzz2022-0691

Feasibility study on rapid screening of learning disabilities by number cancellation test in combination with machine learning algorithm

WANG Hongan¹, YU Dongchuan¹, LIU Fulin¹, CHI Xia^2,3

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Abstract

Objective To explore the feasibility of a rapid screening method using number cancellation test (NCT) in combination with machine learning algorithm for learning disabilities (LD), in order to provide ideas for rapid screening of LD. Methods A total of 414 children aged 7-12 years were randomly recruited in Nanjing, and were asked to complete the NCT, in which 18 parameters were measured to evaluate individual's abilities during NCT. Then, these parameters were considered as classification features to explore the classification of LD by seven machine learning algorithms, including linear discriminant analysis, naive Bayes, K-nearest neighbor, neural network, support vector machine, decision tree and random forest. Finally, the classification performance among different machine learning algorithms was compared. Result Among the seven machine learning algorithms, random forest was outstanding for screening LD, and the accuracy and AUC were 0.83 and 0.92, respectively. Conclusion NCT in combination with random forest algorithm can be applied as a rapid screening method (completed within 2 minutes) for LD,which further proves the feasibility of machine learing in disease prediction.

Key words

learning disabilities / machine learning / rapid screening / number cancellation test / school-age children

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WANG Hongan, YU Dongchuan, LIU Fulin, CHI Xia. Feasibility study on rapid screening of learning disabilities by number cancellation test in combination with machine learning algorithm[J]. Chinese Journal of Child Health Care. 2023, 31(6): 595-600 https://doi.org/10.11852/zgetbjzz2022-0691

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