Feature Signature Discovery for Autism Detection: An Automated Machine Learning Based Feature Ranking Framework

Abstract

Autism spectrum disorder is the most used umbrella term for a myriad of neuro-degenerative/developmental conditions typified by inappropriate social behavior, lack of communication/comprehension skills, and restricted mental and emotional maturity. The intriguing factor of this disorder is attributed to the fact that it can be detected only by close monitoring of developmental milestones after childbirth. Moreover, the exact causes for the occurrence of this neurodevelopmental condition are still unknown. Besides, autism is prevalent across individuals irrespective of ethnicity, genetic/familial history, and economic/educational background. Although research suggests that autism is genetic in nature and early detection of this disorder can greatly enhance the independent lifestyle and societal adaptability of affected individuals, there is still a great dearth of information to support the statement of proven facts and figures. This research work places emphasis on the application of automated machine learning incorporated with feature ranking techniques to generate significant feature signatures for the early detection of autism. Publicly available datasets based on the Q-chat scores of individuals across diverse age groups—toddlers, children, adolescents, and adults have been employed in this study. A machine learning framework based on automated hyperparameter optimization is proposed in this work to rank the potential nonclinical markers for autism. Moreover, this study aimed at ranking the AutoML models based on Mathew’s correlation coefficient and balanced accuracy via which nonclinical markers were identified from these datasets. Besides, the feature signatures and their significance in distinguishing between classes are being reported for the first time in autism detection. The proposed framework yielded ∼90% MCC and ∼95% balanced accuracy across all four age groups of autism datasets. Deep learning approaches have yielded a maximum of 92.7% accuracy on the same datasets but are limited in their ability to extract significant markers, have not reported on MCC for unbalanced data, and cannot adapt automatically to new data entries. However, AutoML approaches are more flexible, easier to implement, and provide automated optimization, thereby yielding the highest accuracy with minimal user intervention.

Enco – Standardization Data Pre-Processing Technique in Autism Spectrum Disorder Detection

“…The accuracy and sensitivity scores for various machine learning classifier models are displayed in The previous work (21) shows 84, 88, 86 % of accuracy with SVM, KNN and ANN classifier respectively using replace missing values by mean pre-processing techniques for diabetes prediction. (22) shows 92 % of accuracy with RF model for ASD and (23) combines mean imputation techniques and feature ranking method to select features and shows almost 95.3% of accuracy with RF classifier in ASD. In (24) LDA pre-processing technique with KNN model and artificial algorithms for predicting ASD gives 88% of accuracy.…”

Machine Learning Based Developmental Capability Prediction: A Diagnosis to the Learning Capacity Disorder for Specially-Abled Children

“…The three-phase approach includes dataset identification and preprocessing techniques, data modeling using machine learning approaches like multilayer perceptron and evaluating the performance of the model. This study in [18,19] focuses on creating significant feature signatures for the early detection of autism by applying automated machine learning along with feature ranking approaches on the Q-chat-based dataset.…”

Detection of autism spectrum disorder (ASD) in children and adults using machine learning

“…Application of intelligent approaches present advanced ways to economically detect ASD effected children and adults 44 . Models have been proposed in the literature describing application of different methods and approaches for ASD detection like structural MRI 45 , neural networks 46 , machine learning [47][48][49] , deep learning 50 , transfer learning 51,52 and IoT 53 . All these techniques have been applied to detect ASD with reasonable accuracy in children and adults but faced limitations of data acquisition as hospitals hesitate or refuse to share patient records due to organizational policies and regional data protection legislations.…”