A machine learning approach to predict autism spectrum disorder
Figure: User interface of ASD Screening application.
Abstract
In present day Autism Spectrum Disorder (ASD) is gaining its momentum faster than ever. Detecting autism traits through screening tests is very expensive and time consuming. With the advancement of artificial intelligence and machine learning (ML), autism can be predicted at quite early stage. Though number of studies have been carried out using different techniques, these studies didn’t provide any definitive conclusion about predicting autism traits in terms of different age groups. Therefore this paper aims to propose an effective prediction model based on ML technique and to develop a mobile application for predicting ASD for people of any age. As outcomes of this research, an autism prediction model was developed by merging Random Forest-CART (Classification and Regression Trees) and Random Forest-ID3 (Iterative Dichotomiser 3) and also a mobile application was developed based on the proposed prediction model. The proposed model was evaluated with AQ10 dataset and 250 real dataset collected from people with and without autistic traits. The evaluation results showed that the proposed prediction model provide better results in terms of accuracy, specificity, sensitivity, precision and false positive rate (FPR) for both kinds of datasets.
In present day Autism Spectrum Disorder (ASD) is gaining its momentum faster than ever. Detecting autism traits through screening tests is very expensive and time consuming. With the advancement of artificial intelligence and machine learning (ML), autism can be predicted at quite early stage. Though number of studies have been carried out using different techniques, these studies didn’t provide any definitive conclusion about predicting autism traits in terms of different age groups. Therefore this paper aims to propose an effective prediction model based on ML technique and to develop a mobile application for predicting ASD for people of any age. As outcomes of this research, an autism prediction model was developed by merging Random Forest-CART (Classification and Regression Trees) and Random Forest-ID3 (Iterative Dichotomiser 3) and also a mobile application was developed based on the proposed prediction model. The proposed model was evaluated with AQ10 dataset and 250 real dataset collected from people with and without autistic traits. The evaluation results showed that the proposed prediction model provide better results in terms of accuracy, specificity, sensitivity, precision and false positive rate (FPR) for both kinds of datasets.
@inproceedings{omar2019machine,title={A machine learning approach to predict autism spectrum disorder},doi={https://ieeexplore.ieee.org/document/8679454},author={Omar, Kazi Shahrukh and Mondal, Prodipta and Khan, Nabila Shahnaz and Rizvi, Md Rezaul Karim and Islam, Md Nazrul},booktitle={International conference on electrical, computer and communication engineering (ECCE)},pages={1--6},year={2019},organization={IEEE},}
A machine learning approach to predict autism spectrum disorderIn International conference on electrical, computer and communication engineering (ECCE), 2019