A machine learning autism classification based on logistic regression analysis

Fadi Thabtah, Neda Abdelhamid, David Peebles

Research output: Contribution to journalArticlepeer-review

76 Citations (Scopus)


Autistic Spectrum Disorder (ASD) is a neurodevelopmental condition associated with significant healthcare costs; early diagnosis could substantially reduce these. The economic impact of autism reveals an urgent need for the development of easily implemented and effective screening methods. Therefore, time-efficient ASD screening is imperative to help health professionals and to inform individuals whether they should pursue formal clinical diagnosis. Presently, very limited autism datasets associated with screening are available and most of them are genetic in nature. We propose new machine learning framework related to autism screening of adults and adolescents that contain vital features and perform predictive analysis using Logistic Regression to reveal important information related to autism screening. We also perform an in-depth feature analysis on the two datasets using information gain (IG) and chi square testing (CHI) to determine the influential features that can be utilized in screening for autism. Results obtained reveal that machine learning technology was able to generate classification systems that have acceptable performance in terms of sensitivity, specificity and accuracy among others.
Original languageEnglish
Article number12
Number of pages11
JournalHealth Information Science and Systems
Issue number1
Early online date1 Jun 2019
Publication statusPublished - 1 Dec 2019


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