Exploring message framing to engage parents in early screening for autism spectrum disorder

Validation of the social communication questionnaire amongst Nigerian adolescents

Few autism spectrum disorder (ASD) screening tools have been developed and validated in Africa. This study aimed to examine the psychometric properties of the Social Communication Questionnaire (SCQ) when used with Nigerian adolescents. Parents and caregivers of two hundred and five adolescents completed the SCQ Lifetime form while the adolescents were assessed for ASD using the Autism Diagnostic Observation Schedule, 2nd Edition (ADOS-2). Factor structure and convergent and discriminative validity were examined, along with the sensitivity and specificity of the SCQ in identifying participants with an autism spectrum disorder. The confirmatory factor analysis (CFA) was used to examine the factor structure, while logistic regression and Pearson's correlation coefficient were used to examine the validities. The SCQ had good internal consistency, discriminative, and convergent validity. A cut-off score of 10 revealed sensitivity = 0.81 and specificity = 0.88 for the identification of autism spectrum disorder. AUC was 0.83, p < 0.001, 95% CI [0.77, 0.90]. The results of this study provide evidence to support the retention of the original four factors of the SCQ. The SCQ has good psychometric properties when used with Nigerian adolescents.

Comparison of classification algorithms for predicting autistic spectrum disorder using WEKA modeler

BACKGROUND

In healthcare area, big data, if integrated with machine learning, enables health practitioners to predict the result of a disorder or disease more accurately. In Autistic Spectrum Disorder (ASD), it is important to screen the patients to enable them to undergo proper treatments as early as possible. However, difficulties may arise in predicting ASD occurrences accurately, mainly caused by human errors. Data mining, if embedded into health screening practice, can help to overcome the difficulties. This study attempts to evaluate the performance of six best classifiers, taken from existing works, at analysing ASD screening training dataset.

RESULT

We tested Naive Bayes, Logistic Regression, KNN, J48, Random Forest, SVM, and Deep Neural Network algorithms to ASD screening dataset and compared the classifiers' based on significant parameters; sensitivity, specificity, accuracy, receiver operating characteristic, area under the curve, and runtime, in predicting ASD occurrences. We also found that most of previous studies focused on classifying health-related dataset while ignoring the missing values which may contribute to significant impacts to the classification result which in turn may impact the life of the patients. Thus, we addressed the missing values by implementing imputation method where they are replaced with the mean of the available records found in the dataset.

CONCLUSION

We found that J48 produced promising results as compared to other classifiers when tested in both circumstances, with and without missing values. Our findings also suggested that SVM does not necessarily perform well for small and simple datasets. The outcome is hoped to assist health practitioners in making accurate diagnosis of ASD occurrences in patients.