Predictive assessment of autism using unsupervised machine learning models

Pratap, Anju; Selvi, C. S. Kanimozhi

doi:10.1504/ijaip.2014.062174

Cited by 18 publications

(8 citation statements)

References 7 publications

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“…A sufficient number of studies have been performed to detect ASD by both supervised [2,[89][90][91][92][93][94][95][96][97] and unsupervised machine [98,99] learning methods. In our study, supervised machine learning has mainly been used for the detection of ASD through behavioral or neuroimaging data, whereas unsupervised machine learning was deployed for predicting ASD assessment.…”

Section: Comparison With Prior Workmentioning

confidence: 99%

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Informing Developmental Milestone Achievement for Children With Autism: Machine Learning Approach

et al. 2021

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Background Care for children with autism spectrum disorder (ASD) can be challenging for families and medical care systems. This is especially true in low- and- middle-income countries such as Bangladesh. To improve family–practitioner communication and developmental monitoring of children with ASD, mCARE (Mobile-Based Care for Children with Autism Spectrum Disorder Using Remote Experience Sampling Method) was developed. Within this study, mCARE was used to track child milestone achievement and family sociodemographic assets to inform mCARE feasibility/scalability and family asset–informed practitioner recommendations. Objective The objectives of this paper are threefold. First, it documents how mCARE can be used to monitor child milestone achievement. Second, it demonstrates how advanced machine learning models can inform our understanding of milestone achievement in children with ASD. Third, it describes family/child sociodemographic factors that are associated with earlier milestone achievement in children with ASD (across 5 machine learning models). Methods Using mCARE-collected data, this study assessed milestone achievement in 300 children with ASD from Bangladesh. In this study, we used 4 supervised machine learning algorithms (decision tree, logistic regression, K-nearest neighbor [KNN], and artificial neural network [ANN]) and 1 unsupervised machine learning algorithm (K-means clustering) to build models of milestone achievement based on family/child sociodemographic details. For analyses, the sample was randomly divided in half to train the machine learning models and then their accuracy was estimated based on the other half of the sample. Each model was specified for the following milestones: Brushes teeth, Asks to use the toilet, Urinates in the toilet or potty, and Buttons large buttons. Results This study aimed to find a suitable machine learning algorithm for milestone prediction/achievement for children with ASD using family/child sociodemographic characteristics. For Brushes teeth, the 3 supervised machine learning models met or exceeded an accuracy of 95% with logistic regression, KNN, and ANN as the most robust sociodemographic predictors. For Asks to use toilet, 84.00% accuracy was achieved with the KNN and ANN models. For these models, the family sociodemographic predictors of “family expenditure” and “parents’ age” accounted for most of the model variability. The last 2 parameters, Urinates in toilet or potty and Buttons large buttons, had an accuracy of 91.00% and 76.00%, respectively, in ANN. Overall, the ANN had a higher accuracy (above ~80% on average) among the other algorithms for all the parameters. Across the models and milestones, “family expenditure,” “family size/type,” “living places,” and “parent’s age and occupation” were the most influential family/child sociodemographic factors. Conclusions mCARE was successfully deployed in a low- and middle-income country (ie, Bangladesh), providing parents and care practitioners a mechanism to share detailed information on child milestones achievement. Using advanced modeling techniques this study demonstrates how family/child sociodemographic elements can inform child milestone achievement. Specifically, families with fewer sociodemographic resources reported later milestone attainment. Developmental science theories highlight how family/systems can directly influence child development and this study provides a clear link between family resources and child developmental progress. Clinical implications for this work could include supporting the larger family system to improve child milestone achievement.

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Section: Comparison With Prior Workmentioning

confidence: 99%

“…Besides these studies, rule-based [93] classification approaches such as decision trees, random forest, and linear discriminant analysis [94][95][96][97] have been used to detect ASD. By contrast, unsupervised machine learning has been used for predicting ASD assessment or analysis of ASD problem in children [98,99].…”

Section: Comparison With Prior Workmentioning

confidence: 99%

Informing Developmental Milestone Achievement for Children With Autism: Machine Learning Approach

et al. 2021

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“…It processes information in a complex non-linear fashion and are excellent for the typical types of data collected with rating scales. Likewise, the process of learning and generalization in ANN is analogous to a clinical decision making process where, clinicians learnt about a disorder by examining different cases and expresses expertise through diagnosing new cases [19]. Hence, these properties make them to widely apply in modeling, medical diagnosis problem, such that the models can support or assist doctors with their diagnosis by using the reported symptoms.…”

Section: Artificial Neural Networkmentioning

confidence: 99%

“…Artificial intelligence solves complex problems by simulating natural intelligence through reasoning from historical problems and their solutions. Machine learning is a growing discipline in the field of artificial intelligence, in which a computer machine can learn automatically based on a set of data and make decisions by recognizing complex patterns based on the data [1,19]. Machine learning systems built using medical AI programs can help the healthcare workers in assisting tasks that depend on data and knowledge manipulation.…”

Section: Introductionmentioning

confidence: 99%

Possibilistic LVQ Neural Network – An Application to Childhood Autism Grading

Selvi¹,

Pratap²

2016

NNW

Self Cite

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Precise wind energy potential assessment is vital for wind energy generation and planning and development of new wind power plants. This work proposes and evaluates a novel two-stage method for location-specific wind energy potential assessment. It combines accurate statistical modelling of annual wind direction distribution in a given location with supervised machine learning of efficient estimators that can approximate energy efficiency coefficients from the parameters of optimized statistical wind direction models. The statistical models are optimized using differential evolution and energy efficiency is approximated by evolutionary fuzzy rules.

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“…ASD diagnosis problems can be solved by applying classification using ML algorithms [8], [9]. Classification based models can reason with uncertainty, partial truth, imprecision and approximation [5], [7].…”

Section: Introductionmentioning

confidence: 99%

Machine Learning based Autism Grading for Clinical Decision Making

Kanimozhiselvi*,

Jayaprakash

2019

IJRTE

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Autism spectrum disorder is a pervasive developmental disorder that affects the behavioral and communication function of the children. It shows poor performance in communication, social and cognitive abilities, which are generally characterized by developmental delays and abnormal activities in their regular work. Early intervention can reduce the autism spectrum disorders. Machine learning techniques are used to detect autistic features in childhood. The prediction models are implemented as classification problem in which model is constructed by using real-time autism dataset. The proposed work is use Backpropagation and learning vector quantization with different distance measures like Euclidean Distance, Manhattan Distance, and City Block Distance to predict whether a child has autism spectrum disorder and also defines the grade of the autism. So that it can be supported for the clinical decision making. It enables automated clinical autism spectrum disorder diagnostic process using machine learning models.

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Predictive assessment of autism using unsupervised machine learning models

Cited by 18 publications

References 7 publications

Informing Developmental Milestone Achievement for Children With Autism: Machine Learning Approach

Informing Developmental Milestone Achievement for Children With Autism: Machine Learning Approach

Possibilistic LVQ Neural Network – An Application to Childhood Autism Grading

Machine Learning based Autism Grading for Clinical Decision Making

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