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A new interesting article has been published in Health Inf Sci Syst. 2019 Jun 1; 7(1):12. doi: 10.1007/s13755-019-0073-5. eCollection 2019 Dec. and titled:

A machine learning autism classification based on logistic regression analysis.

Authors of this article are:

Thabtah F, Abdelhamid N, Peebles D.

A summary of the article is shown below:

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.

Check out the article’s website on Pubmed for more information:

[link-preview url=https://www.ncbi.nlm.nih.gov/pubmed/31168365 forceshot=true]

This article is a good source of information and a good way to become familiar with topics such as: Autism spectrum disorder; Classification; Clinical decision making; Data mining; Feature analysis; Machine learning; Sensitivity; Specificity.

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