Classifying Autism Spectrum Disorder using Machine Learning Models


Tingyan Deng, Vanderbilt University, USA


Autistic Spectrum Disorder (ASD) is a developmental disability, which can affect communication and behavior, causing significant social, communication, and behavior challenge. From a rare childhood disorder, ASD has evolved into a disorder that is found, according to the National Institute of Health, in 1% to 2% of the population in high income countries. A potential early and accurate diagnosis can not only help doctors to find the disease early, leading to a more on time treatment to the patient, but also can save significant healthcare costs for the patients. With the rapid growth of ASD cases, many open-source ASD related datasets were created for scientists and doctors to investigate this disease. Autistic Spectrum Disorder Screening Data for Adult is a well-known dataset, which contains 20 features to be utilized for further analysis on the potential cause and prediction of ASD. In this paper, we developed an Autism classification algorithm based on logistic regression model. Our model starts with featuring engineering to extract deep information from the dataset and then applied a modified logistic regression classifier to the data. The model can predict the ASD in an average F1 score of 0.97, which displays the superiority and feasibility of the proposed model. Besides, the data visualization technique was used to displays several feature distributions images for people to better understand the data and related feature engineering.


ASD, Logistic Regression, Classification, Machine Learning, Neurodiversity.

Full Text  Volume 11, Number 3