Applications of Supervised Machine Learning in Autism Spectrum Disorder Research: a Review <s> Naïve Bayes <s> Currently there are no brief, self-administered instruments for measuring the degree to which an adult with normal intelligence has the traits associated with the autistic spectrum. In this paper, we report on a new instrument to assess this: the Autism-Spectrum Quotient (AQ). Individuals score in the range 0–50. Four groups of subjects were assessed: Group 1: 58 adults with Asperger syndrome (AS) or high-functioning autism (HFA); Group 2: 174 randomly selected controls. Group 3: 840 students in Cambridge University; and Group 4: 16 winners of the UK Mathematics Olympiad. The adults with AS/HFA had a mean AQ score of 35.8 (SD = 6.5), significantly higher than Group 2 controls (M = 16.4, SD = 6.3). 80% of the adults with AS/HFA scored 32+, versus 2% of controls. Among the controls, men scored slightly but significantly higher than women. No women scored extremely highly (AQ score 34+) whereas 4% of men did so. Twice as many men (40%) as women (21%) scored at intermediate levels (AQ score 20+). Among the AS/HFA group, male and female scores did not differ significantly. The students in Cambridge University did not differ from the randomly selected control group, but scientists (including mathematicians) scored significantly higher than both humanities and social sciences students, confirming an earlier study that autistic conditions are associated with scientific skills. Within the sciences, mathematicians scored highest. This was replicated in Group 4, the Mathematics Olympiad winners scoring significantly higher than the male Cambridge humanities students. 6% of the student sample scored 327plus; on the AQ. On interview, 11 out of 11 of these met three or more DSM-IV criteria for AS/HFA, and all were studying sciences/mathematics, and 7 of the 11 met threshold on these criteria. Test—retest and interrater reliability of the AQ was good. The AQ is thus a valuable instrument for rapidly quantifying where any given individual is situated on the continuum from autism to normality. Its potential for screening for autism spectrum conditions in adults of normal intelligence remains to be fully explored. <s> BIB001 </s> Applications of Supervised Machine Learning in Autism Spectrum Disorder Research: a Review <s> Naïve Bayes <s> Considering the raising socio-economic burden of autism spectrum disorder (ASD), timely and evidence-driven public policy decision-making and communication of the latest guidelines pertaining to the treatment and management of the disorder is crucial. Yet evidence suggests that policy makers and medical practitioners do not always have a good understanding of the practices and relevant beliefs of ASD-afflicted individuals’ carers who often follow questionable recommendations and adopt advice poorly supported by scientific data. The key goal of the present work is to explore the idea that Twitter, as a highly popular platform for information exchange, could be used as a data-mining source to learn about the population affected by ASD—their behaviour, concerns, needs, etc. To this end, using a large data set of over 11 million harvested tweets as the basis for our investigation, we describe a series of experiments which examine a range of linguistic and semantic aspects of messages posted by individuals interested in ASD. Our findings, the first of their nature in the published scientific literature, strongly motivate additional research on this topic and present a methodological basis for further work. <s> BIB002
Naïve Bayes is a probabilistic classifier that utilizes Bayes theorem to make the (naïve) assumption that all input features are linearly independent and then compare the probability of each possible classification given a set of independent features  . The naïve Bayes classifier is relatively simple and computationally inexpensive to train, and it can be competitive with more complex models like the SVM in areas like text categorization, making it still a preferred choice for spam filtering. Naïve Bayes has been used in the reviewed literature to leverage data from social media applications in order to learn more about the ASD community and to determine if ASD can be detected based on samples of writing. Social Media Text mining was applied to Twitter posts by BIB002 to gain knowledge about the ASD community. From an initial dataset of 11 million tweets, the authors used ASD-related keywords to identify over 5.6 million tweets that were ASD related. Of those, 3,493,742 were found to be original tweets created by 1,771,274 unique users. Both ASD and control corpora were found to obey Zipf's law, suggesting that current text representation and analysis tools could be applied. The authors explored message length, word frequency, hashtag analysis, and part-of-speech analysis with classical statistics before investigating tweet classification. After splitting the data in half for training and testing sets, naïve Bayes, ridge logistic regression, and LASSO classification models were conducted. With the ASD-related keywords used to identify the ASD tweets eliminated from the analyses, naïve Bayes and logistic regression achieved approximately 80% accuracy, while LASSO had up to 70% accuracy. The authors suggested that analyses such as these could potentially yield valuable information from within the ASD community (e.g., views on current initiatives and legislature) that may be of interest to public health officials and policy makers.  also used classification algorithms to evaluate whether personal blogs could predict Autism Spectrum Quotient (AQ; BIB001 scores. The AQ is a questionnaire developed to aid in the diagnosis of ASD. Searching Wordpress.com for blogs containing self-reported AQ scores, Bennett identified 75 blogs (35 male, 40 female) posted after 2009 with each blog containing over 10,000 words. Analyses were conducted to determine if blog content could predict whether the authors' AQ scores fell above or below the cutoff point for ASD. Significant differences in the number and type of useful features between male and female bloggers were found. The highest predictive accuracy, 90.9%, was achieved for males using LIWC dimension selection and a naïve Bayes classifier. Females were classified with an 88.7% accuracy using 3-g models with naïve Bayes. These finding show the potential in the use of machine learning to detect ASD from samples of text.