Prediction of Autism Spectrum Disorder Using Feature Selection and Machine Learning Algorithms

2021 ◽  
pp. 133-148
Author(s):  
Mousumi Bala ◽  
Ayesha Aziz Prova ◽  
Mohammad Hanif Ali
Keyword(s):  
Machine Learning ◽  
Autism Spectrum Disorder ◽  
Feature Selection ◽  
Learning Algorithms ◽  
Autism Spectrum ◽  
Machine Learning Algorithms ◽  
Spectrum Disorder

Diagnostic test accuracy for use of machine learning in diagnosis of autism spectrum disorder: A Systematic Review and Meta-Analysis (Preprint)

2019 ◽  
Author(s):  
Sun Jae Moon ◽  
Jin Seub Hwang ◽  
Rajesh Kana ◽  
John Torous ◽  
Jung Won Kim
Keyword(s):  
Machine Learning ◽  
Systematic Review ◽  
Autism Spectrum Disorder ◽  
Meta Analysis ◽  
Learning Algorithms ◽  
Structural Mri ◽  
Autism Spectrum ◽  
Machine Learning Algorithms ◽  
Spectrum Disorder ◽  
Test Accuracy

BACKGROUND Over the recent years, machine learning algorithms have been more widely and increasingly applied in biomedical fields. In particular, its application has been drawing more attention in the field of psychiatry, for instance, as diagnostic tests/tools for autism spectrum disorder. However, given its complexity and potential clinical implications, there is ongoing need for further research on its accuracy. OBJECTIVE The current study aims to summarize the evidence for the accuracy of use of machine learning algorithms in diagnosing autism spectrum disorder (ASD) through systematic review and meta-analysis. METHODS MEDLINE, Embase, CINAHL Complete (with OpenDissertations), PsyINFO and IEEE Xplore Digital Library databases were searched on November 28th, 2018. Studies, which used a machine learning algorithm partially or fully in classifying ASD from controls and provided accuracy measures, were included in our analysis. Bivariate random effects model was applied to the pooled data in meta-analysis. Subgroup analysis was used to investigate and resolve the source of heterogeneity between studies. True-positive, false-positive, false negative and true-negative values from individual studies were used to calculate the pooled sensitivity and specificity values, draw SROC curves, and obtain area under the curve (AUC) and partial AUC. RESULTS A total of 43 studies were included for the final analysis, of which meta-analysis was performed on 40 studies (53 samples with 12,128 participants). A structural MRI subgroup meta-analysis (12 samples with 1,776 participants) showed the sensitivity at 0.83 (95% CI-0.76 to 0.89), specificity at 0.84 (95% CI -0.74 to 0.91), and AUC/pAUC at 0.90/0.83. An fMRI/deep neural network (DNN) subgroup meta-analysis (five samples with 1,345 participants) showed the sensitivity at 0.69 (95% CI- 0.62 to 0.75), the specificity at 0.66 (95% CI -0.61 to 0.70), and AUC/pAUC at 0.71/0.67. CONCLUSIONS Machine learning algorithms that used structural MRI features in diagnosis of ASD were shown to have accuracy that is similar to currently used diagnostic tools.

scholarly journals Accuracy of machine learning algorithms for the diagnosis of autism spectrum disorder based on cerebral sMRI, rs-fMRI, and EEG: protocols for three systematic reviews and meta-analyses

2021 ◽  
Author(s):  
Amir Valizadeh ◽  
Mana Moassefi ◽  
Amin Nakhostin-Ansari ◽  
Iman Menbari Oskoie ◽  
Soheil Heidari Some'eh ◽  
...  
Keyword(s):  
Machine Learning ◽  
Autism Spectrum Disorder ◽  
Diagnostic Accuracy ◽  
Learning Algorithms ◽  
Grey Literature ◽  
Autism Spectrum ◽  
Machine Learning Algorithms ◽  
Spectrum Disorder ◽  
Cross Sectional ◽  
Medicine Research

Objective: To determine the diagnostic accuracy of the applied machine learning algorithms for the diagnosis of autism spectrum disorder (ASD) based on structural magnetic resonance imaging (sMRI), resting-state functional MRI (rs-fMRI), and electroencephalography (EEG). Methods: We will include cross-sectional studies (both single-gates and two-gates) that have evaluated the diagnostic accuracy of machine learning algorithms on the sMRI data of ASD patients regardless of age, sex, and ethnicity. On the 22nd of May 2021, we searched Embase, MEDLINE, APA PsycINFO, IEEE Xplore, Scopus, and Web of Science for eligible studies. We also searched grey literature within various sources. We will use an adapted version of the Quality Assessment of Diagnostic Accuracy Studies (QUADAS-2) tool to assess the risk of bias and applicability. Data will be synthesized using the relatively new Split Component Synthesis (SCS) method. We plan to assess heterogeneity using the I2 statistics and assess publication bias using trim and fill tests combined with ln DOR. Certainty of evidence will be assessed using the GRADE approach for diagnostic studies. Funding: These studies are funded by Sports Medicine Research Center, Tehran, Iran. Registration: PROSPERO submission IDs: 262575, 262825, and 262831.

scholarly journals A comparison of machine learning algorithms for the surveillance of autism spectrum disorder

PLoS ONE ◽  
2019 ◽  
Vol 14 (9) ◽  
pp. e0222907 ◽  
Author(s):  
Scott H. Lee ◽  
Matthew J. Maenner ◽  
Charles M. Heilig
Keyword(s):  
Machine Learning ◽  
Autism Spectrum Disorder ◽  
Learning Algorithms ◽  
Autism Spectrum ◽  
Machine Learning Algorithms ◽  
Spectrum Disorder

scholarly journals Optimization of Machine Learning Techniques on Autism Spectrum Disorder with Swarm Intelligence Based Feature Selection

2019 ◽  
Vol 8 (2) ◽  
pp. 6248-6251
Keyword(s):  
Machine Learning ◽  
Autism Spectrum Disorder ◽  
Feature Selection ◽  
Swarm Intelligence ◽  
Autism Spectrum ◽  
Machine Learning Algorithms ◽  
Spectrum Disorder ◽  
Machine Learning Techniques ◽  
Learning Techniques ◽  
Dsm V

This paper is a study on the various machine learning algorithms in order to perform ASD (Autism spectrum Disorder) as per the DSM-V standards. ASD occurs more frequently among children and in order to diagnose this with better accuracy, the study on binary firefly algorithm, a swarm intelligence based wrapper feature selection algorithm is used to obtain best results with optimum feature subsets. This paper will provide overall result after applying it to all types of machine learning models on supervised learning.

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