A Machine Learning Approach to Predict Autism Spectrum Disorder

Oma, Kazi Shahrukh; Mondal, Prodipta; Khan, Nabila Shahnaz; Rizvi, Md. Rezaul Karim; Islam, Muhammad Nazrul

doi:10.1109/ecace.2019.8679454

Cited by 139 publications

(74 citation statements)

References 12 publications

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“…Previous studies have applied machine learning techniques to examine whether the process of diagnosing ASD can be improved by statistically identifying reduced subsets of features from existing diagnostic instruments reaching from self-administered screening questionnaires to clinician-administered diagnostic tools (for a recent overview, see Thabtah 32 ). A few authors have shown that efficiency and accessibility of existing pre-diagnostic screening questionnaires such as the Autism-Spectrum Quotient (AQ) [33][34][35] or the Social Responsiveness Scale (SRS) 36,37 can be improved using machine learning. Similar machine learning experiments have been run to identify subsets of behavioral features from clinician-administered diagnostic tools, namely ADOS (Module 1 to 3) [38][39][40][41][42] and ADI-R 36,39,43 .…”

mentioning

confidence: 99%

Identifying predictive features of autism spectrum disorders in a clinical sample of adolescents and adults using machine learning

Küpper

Stroth

Wolff

et al. 2020

Sci Rep

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Diagnosing autism spectrum disorders (ASD) is a complicated, time-consuming process which is particularly challenging in older individuals. one of the most widely used behavioral diagnostic tools is the Autism Diagnostic observation Schedule (ADoS). previous work using machine learning techniques suggested that ASD detection in children can be achieved with substantially fewer items than the original ADOS. Here, we expand on this work with a specific focus on adolescents and adults as assessed with the ADOS Module 4. We used a machine learning algorithm (support vector machine) to examine whether ASD detection can be improved by identifying a subset of behavioral features from the ADOS Module 4 in a routine clinical sample of N = 673 high-functioning adolescents and adults with ASD (n = 385) and individuals with suspected ASD but other best-estimate or no psychiatric diagnoses (n = 288). We identified reduced subsets of 5 behavioral features for the whole sample as well as age subgroups (adolescents vs. adults) that showed good specificity and sensitivity and reached performance close to that of the existing ADOS algorithm and the full ADOS, with no significant differences in overall performance. These results may help to improve the complicated diagnostic process of ASD by encouraging future efforts to develop novel diagnostic instruments for ASD detection based on the identified constructs as well as aiding clinicians in the difficult question of differential diagnosis. Autism Spectrum Disorders (ASD) comprise a range of pervasive neurodevelopmental disorders with a population prevalence of approximately 1% 1. They are characterized by early-onset persistent impairments in social communication and interaction as well as the presence of restricted, repetitive behaviors or interests 2,3. Diagnosing ASD is a complicated, lengthy and time-consuming process, which requires outstanding and specific clinical expertise 4,5. Although research makes constant progress in understanding the underlying genetic and neurobiological factors associated with ASD, there are currently no reliable biological markers for ASD and the diagnosis remains based on behavioral symptoms 1,6,7. The current so-called "gold standard" of ASD diagnosis comprises the use of various standardized diagnostic instruments that assist clinicians in reaching a best-estimate clinical diagnosis 7-9. Two of the most widely used diagnostic instruments are the Autism Diagnostic Observation Schedule (ADOS respectively ADOS-2 for the revised second edition) 10,11 and the Autism Diagnostic Interview

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mentioning

confidence: 99%

Identifying predictive features of autism spectrum disorders in a clinical sample of adolescents and adults using machine learning

Küpper

Stroth

Wolff

et al. 2020

Sci Rep

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“…As shown in Table I, AQ screening is the most efficient method with only ten questions, which require less time to complete than other methods. Further, AQ deals with many age segments, and each of them has a specific questionnaire, which will be discussed in detail in the methodology section [15,23].…”

Section: F Comparison Of Asd Diagnosing Methodsmentioning

confidence: 99%

Autism Spectrum Disorder Diagnosis using Optimal Machine Learning Methods

Alteneiji¹,

Mohammed²,

Usman³

2020

IJACSA

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Autism spectrum disorder (ASD) is the disorder of communication and behavior that affects children and adults. It can be diagnosed at any stage of life. Most importantly, the first two years of life, regardless of ethnicity, race, or economic groups. There are different variations of ASD according to the severity and type of symptoms experienced by people. It is a lifelong disorder, but treatment and services can improve the symptoms. The literature focuses on one of the main methods used by physicians to diagnose ASD. Many types of research and medical reports have been reviewed; however, a few of them only give good medical results for the strong differentiation of ASD from healthy people. This paper focuses on using machine learning algorithms to predict an individual with specific ASD symptoms. The target is to predict an individual with specific ASD symptoms and finding the best machine learning model for diagnosis. Further, the paper aims to make the autism diagnosis faster to deliver the required treatment at an early stage of child development.

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“…An significant difference between PCA and ICA were related to the quantity of components used in the technique. Kazi Shahrukh Omar et al [9] proposed a model by merging random forest-ID3, Random forest-CART for predicting the autism traits. The evaluation done with AQ10 dataset and 250 real dataset collected from various persons.…”

Section: Literature Reviewmentioning

confidence: 99%

Effective Implementation of Pre-Processing Techniques in Machine Learning for Autism Spectrum Disorder

Priya*,

Radhika

2020

IJITEE

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Autism Spectrum disorder (ASD) is a neurobiological developmental disorder is symbolize by means of the impairment of social interaction, stereotypic behaviours, and communiqué lack. Early deduction of ASD will enhance the fine of lifestyles of the affected person. The objective of the paper is to focus on the application of various Machine Learning strategies applied for the autism dataset for diagnosing ASD. In this study, the effective pre-processing techniques One-hot encoding, Splitting and Scaling are used to standardize the dataset and the Principal Component Analysis (PCA) evaluator method is applied for the best feature selection. This technique is investigated with various Machine learning techniques like Random Forest, SVM, Logistic Regression, KNN, Naive Bayes. Comparatively, the effective Pre-Processing technique with Random Forest model shows the better accuracy of 92% in diagnosing ASD. When with other metrics such as accuracy, precision, recall, F1-score, ROC, error rate.

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A Machine Learning Approach to Predict Autism Spectrum Disorder

Cited by 139 publications

References 12 publications

Identifying predictive features of autism spectrum disorders in a clinical sample of adolescents and adults using machine learning

Identifying predictive features of autism spectrum disorders in a clinical sample of adolescents and adults using machine learning

Autism Spectrum Disorder Diagnosis using Optimal Machine Learning Methods

Effective Implementation of Pre-Processing Techniques in Machine Learning for Autism Spectrum Disorder

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