Prediction Of Autism Spectrum Disorder Research Articles

Retraction: Preliminary evaluation of a novel nine-biomarker profile for the prediction of autism spectrum disorder.

Retraction: Preliminary evaluation of a novel nine-biomarker profile for the prediction of autism spectrum disorder.

Detecting Autism from Head Movements using Kinesics.

Head movements play a crucial role in social interactions. The quantification of communicative movements such as nodding, shaking, orienting, and backchanneling is significant in behavioral and mental health research. However, automated localization of such head movements within videos remains challenging in computer vision due to their arbitrary start and end times, durations, and frequencies. In this work, we introduce a novel and efficient coding system for head movements, grounded in Birdwhistell's kinesics theory, to automatically identify basic head motion units such as nodding and shaking. Our approach first defines the smallest unit of head movement, termed kine, based on the anatomical constraints of the neck and head. We then quantify the location, magnitude, and duration of kines within each angular component of head movement. Through defining possible combinations of identified kines, we define a higher-level construct, kineme, which corresponds to basic head motion units such as nodding and shaking. We validate the proposed framework by predicting autism spectrum disorder (ASD) diagnosis from video recordings of interacting partners. We show that the multi-scale property of the proposed framework provides a significant advantage, as collapsing behavior across temporal scales reduces performance consistently. Finally, we incorporate another fundamental behavioral modality, namely speech, and show that distinguishing between speaking- and listening-time head movementsments significantly improves ASD classification performance.

Deep learning approach to predict autism spectrum disorder: a systematic review and meta-analysis

BackgroundThe use of the deep learning (DL) approach has been suggested or applied to identify childhood autism spectrum disorder (ASD). The capacity to predict ASD, however, differs across investigations. Our study’s objective was to conduct a meta-analysis to determine the DL for ASD in children’s classification accuracy.MethodsEligibility criteria were designed according to the purpose of the meta-analysis; PubMed, EMBASE, Cochrane Library, and Web of Science Database were searched for articles published up to April 16, 2023, on the accuracy of DL methods for ASD classification. Using the Revised Tool for the Quality Assessment of Diagnostic Accuracy Studies (QUADAS-2) to assess the quality of the included studies. Sensitivity, specificity, areas under the curve (AUC), summary receiver operating characteristic (SROC), and corresponding 95% confidence intervals (CIs) were compiled by using the bivariate random-effects models.ResultsA total of 11 predictive trials based on DL models were included, involving 9495 ASD patients from 6 different databases. According to bivariate random-effects models’ results, the overall sensitivity, specificity, and AUC of the DL technique for ASD were, 0.95 (95% CI = 0.88–0.98), 0.93 (95% CI = 0.85–0.97), and 0.98 (95%CI: 0.97–0.99), respectively. Subgroup analysis results found that different datasets did not cause heterogeneity (meta-regression P = 0.55). The Kaggle dataset’s sensitivity and specificity were 0.94 (95%CI: 0.82-1.00) and 0.91 (95%CI: 0.76-1.00), and with 0.97 (95%CI: 0.92-1.00) and 0.97 (95%CI: 0.92-1.00) for ABIDE dataset.ConclusionsDL techniques has satisfactory sensitivity, specificity, and AUC in ASD classification. However, the major heterogeneity of the included studies limited the effectiveness of this meta-analysis. Further trials need to be performed to demonstrate the clinical practicability of DL diagnosis.

Data-centric automated approach to predict autism spectrum disorder based on selective features and explainable artificial intelligence.

Autism spectrum disorder (ASD) is a neurodevelopmental condition marked by notable challenges in cognitive function, understanding language, recognizing objects, interacting with others, and communicating effectively. Its origins are mainly genetic, and identifying it early and intervening promptly can reduce the necessity for extensive medical treatments and lengthy diagnostic procedures for those impacted by ASD. This research is designed with two types of experimentation for ASD analysis. In the first set of experiments, authors utilized three feature engineering techniques (Chi-square, backward feature elimination, and PCA) with multiple machine learning models for autism presence prediction in toddlers. The proposed XGBoost 2.0 obtained 99% accuracy, F1 score, and recall with 98% precision with chi-square significant features. In the second scenario, main focus shifts to identifying tailored educational methods for children with ASD through the assessment of their behavioral, verbal, and physical responses. Again, the proposed approach performs well with 99% accuracy, F1 score, recall, and precision. In this research, cross-validation technique is also implemented to check the stability of the proposed model along with the comparison of previously published research works to show the significance of the proposed model. This study aims to develop personalized educational strategies for individuals with ASD using machine learning techniques to meet their specific needs better.

Comparative Analysis of Machine Learning Models for Prediction of Autism Spectrum Disorder Using Screening Data

Autism spectrum disorder (ASD) is a neurological and developmental disorder that affects how people interact with others, communicate, learn, and behave. ASD prediction is difficult because the diagnostic factors may not be based solely on observation. In this research paper, an in-depth comparative analysis of various machine learning models applied to the task of classifying autism traits was presented. Our study aimed to assess the performance of these models within the context of identifying individuals with autism based on relevant features and data. The machine learning models investigated in this study encompassed Logistic Regression (LR), Random Forest (RF), Support Vector Machine (SVM), k-Nearest Neighbours (KNN), Naive Bayes (NB), and Neural Network (NN). The models were evaluated using six essential classification metrics: accuracy, precision, recall, specificity, F1 score, and AUC score. On the training dataset, our results reveal nuanced performance characteristics. SVM and RF excel in precision and recall, showing promise for accurate autism trait classification. KNN exhibits remarkable specificity, suggesting its potential for minimizing false positives. LR and NB demonstrate balanced performance across multiple metrics, while NN exhibits high precision and recall, albeit with higher computational demands. It was concluded that SVM was the best classification model for autism trait classification.

A Survey on Genetic Disease − Autism Spectrum Disorder Prediction and Classification in Machine Learning

Autism spectrum disorder (ASD) is a hereditary, neurological condition with many aetiologies that manifest in early childhood. Mental illnesses, including anxiety, poor communication, and a lack of recurrent interest, may result from ASD. It can be highly advantageous for children to improve their psychological wellness level if the ASD is recognized in the earlier years of life. Furthermore, machine learning (ML) approaches are now essential for diagnosing and categorising ASD. The creation of computer programmes that can acquire data and utilise it to gain knowledge for oneself is the main goal of this aspect of artificial intelligence. Many scholars have suggested various ML strategies for quickly and accurately detecting the various forms of ASD. This paper presents a survey on ASD prediction and classification using ML methods-based research articles from the year 2016 to 2023. Moreover, the current survey article discusses the performance assessment employing different metrics and made a comparative assessment to determine the ML model’s effectiveness. From this survey, it is identified that Artificial Neural Network model has attained better results than other ML algorithms. Moreover, further ASD studies employing an ML strategy for feature selection, prediction and classification can greatly benefit from this research.

Using AI and ML to Predict Autism Spectrum Disorder

Using AI and ML to Predict Autism Spectrum Disorder

Retraction Note: Prediction of autism spectrum disorder from high-dimensional data using machine learning techniques

Retraction Note: Prediction of autism spectrum disorder from high-dimensional data using machine learning techniques

Machine Learning Prediction of Autism Spectrum Disorder From a Minimal Set of Medical and Background Information

Early identification of the likelihood of autism spectrum disorder (ASD) using minimal information is crucial for early diagnosis and intervention, which can affect developmental outcomes. To develop and validate a machine learning (ML) model for predicting ASD using a minimal set of features from background and medical information and to evaluate the predictors and the utility of the ML model. For this diagnostic study, a retrospective analysis of the Simons Foundation Powering Autism Research for Knowledge (SPARK) database, version 8 (released June 6, 2022), was conducted, including data from 30 660 participants after adjustments for missing values and class imbalances (15 330 with ASD and 15 330 without ASD). The SPARK database contains participants recruited from 31 university-affiliated research clinicals and online in 26 states in the US. All individuals with a professional ASD diagnosis and their families were eligible to participate. The model performance was validated on independent datasets from SPARK, version 10 (released July 21, 2023), and the Simons Simplex Collection (SSC), consisting of 14 790 participants, followed by phenotypic associations. Twenty-eight basic medical screening and background history items present before 24 months of age. Generalizable ML prediction models were developed for detecting ASD using 4 algorithms (logistic regression, decision tree, random forest, and eXtreme Gradient Boosting [XGBoost]). Performance metrics included accuracy, area under the receiver operating characteristics curve (AUROC), sensitivity, specificity, positive predictive value (PPV), and F1 score, offering a comprehensive assessment of the predictive accuracy of the model. Explainable AI methods were applied to determine the effect of individual features in predicting ASD as secondary outcomes, enhancing the interpretability of the best-performing model. The secondary outcome analyses were further complemented by examining differences in various phenotypic measures using nonparametric statistical methods, providing insights into the ability of the model to differentiate between different presentations of ASD. The study included 19 477 (63.5%) male and 11 183 (36.5%) female participants (mean [SD] age, 106 [62] months). The mean (SD) age was 113 (68) months for the ASD group and 100 (55) months for the non-ASD group. The XGBoost (termed AutMedAI) model demonstrated strong performance with an AUROC score of 0.895, sensitivity of 0.805, specificity of 0.829, and PPV of 0.897. Developmental milestones and eating behavior were the most important predictors. Validation on independent cohorts showed an AUROC of 0.790, indicating good generalizability. In this diagnostic study of ML prediction of ASD, robust model performance was observed to identify autistic individuals with more symptoms and lower cognitive levels. The robustness and ML model generalizability results are promising for further validation and use in clinical and population settings.

Resolving autism spectrum disorder (ASD) through brain topologies using fMRI dataset with multi-layer perceptron (MLP)

Autism is a neurodevelopmental disorder that manifests in individuals during childhood and has enduring consequences for their social interactions and communication. The prediction of Autism Spectrum Disorder (ASD) in individuals based on the differences in brain networks and activities have been studied extensively in the recent past, however, with lower accuracies. Therefore in this research, identification at the early stage through computer-aided algorithms to differentiate between ASD and TD patients is proposed. In order to identify features, a Multi-Layer Perceptron (MLP) model is developed which utilizes logistic regression on characteristics extracted from connectivity matrices of subjects derived from fMRI images. The features that significantly contribute to the classification of individuals as having Autism Spectrum Disorder (ASD) or typically developing (TD) are identified by the logistic regression model. To enhance emphasis on essential attributes, an AND operation is integrated. This involves selecting features demonstrating statistical significance across diverse logistic regression analyses conducted on various random distributions. The iterative approach contributes to a comprehensive understanding of relevant features for accurate classification. By implementing this methodology, the estimation of feature importance became more dependable, and the potential for overfitting is moderated through the evaluation of model performance on various subsets of data. It is observed from the experimentation that the highly correlated Left Lateral Occipital Cortex and Right Lateral Occipital Cortex ROIs are only found in ASD. Also, it is noticed that the highly correlated Left Cerebellum Tonsil and Right Cerebellum Tonsil are only found in TD participants. Among the MLP classifier, a recall of 82.61 % is achieved followed by Logistic Regression with an accuracy of 72.46 %. MLP also stands out with a commendable accuracy of 83.57 % and AUC of 0.978.

Machine Learning Approach to Predict Autism Spectrum Disorder

Autism Spectrum Disorder is one of a group of neuro developmental disorders. ASD's are communication , social interactions, range of play and interests, processing and integrating sensory information. In our research we have used the algorithms like Random forest, SVC, ANN, Logistic regression and the Extra trees to predict that which person has the ASD.

A Convolutional Deep Neural Network Approach to Predict Autism Spectrum Disorder Based on Eye-Tracking Scan Paths

Autism spectrum disorder (ASD) is a developmental disorder that encompasses difficulties in communication (both verbal and non-verbal), social skills, and repetitive behaviors. The diagnosis of autism spectrum disorder typically involves specialized procedures and techniques, which can be time-consuming and expensive. The accuracy and efficiency of the diagnosis depend on the expertise of the specialists and the diagnostic methods employed. To address the growing need for early, rapid, cost-effective, and accurate diagnosis of autism spectrum disorder, there has been a search for advanced smart methods that can automatically classify the disorder. Machine learning offers sophisticated techniques for building automated classifiers that can be utilized by users and clinicians to enhance accuracy and efficiency in diagnosis. Eye-tracking scan paths have emerged as a tool increasingly used in autism spectrum disorder clinics. This methodology examines attentional processes by quantitatively measuring eye movements. Its precision, ease of use, and cost-effectiveness make it a promising platform for developing biomarkers for use in clinical trials for autism spectrum disorder. The detection of autism spectrum disorder can be achieved by observing the atypical visual attention patterns of children with the disorder compared to typically developing children. This study proposes a deep learning model, known as T-CNN-Autism Spectrum Disorder (T-CNN-ASD), that utilizes eye-tracking scans to classify participants into ASD and typical development (TD) groups. The proposed model consists of two hidden layers with 300 and 150 neurons, respectively, and underwent 10 rounds of cross-validation with a dropout rate of 20%. In the testing phase, the model achieved an accuracy of 95.59%, surpassing the accuracy of other machine learning algorithms such as random forest (RF), decision tree (DT), K-Nearest Neighbors (KNN), and multi-layer perceptron (MLP). Furthermore, the proposed model demonstrated superior performance when compared to the findings reported in previous studies. The results demonstrate that the proposed model can accurately classify children with ASD from those with TD without human intervention.

Predicting autism spectrum disorder using maternal risk factors: A multi-center machine learning study

Autism spectrum disorder (ASD) is a neurodevelopmental disorder with a complex environmental etiology involving maternal risk factors, which have been combined with machine learning to predict ASD. However, limited studies have considered the factors throughout preconception, perinatal, and postnatal periods, and even fewer have been conducted in multi-center. In this study, five predictive models were developed using 57 maternal risk factors from a cohort across ten cities (ASD:1232, typically developing[TD]: 1090). The extreme gradient boosting model performed best, achieving an accuracy of 66.2 % on the external cohort from three cities (ASD:266, TD:353). The most important risk factors were identified as unstable emotions and lack of multivitamin supplementation using Shapley values. ASD risk scores were calculated based on predicted probabilities from the optimal model and divided into low, medium, and high-risk groups. The logistic analysis indicated that the high-risk group had a significantly increased risk of ASD compared to the low-risk group. Our study demonstrated the potential of machine learning models in predicting the risk for ASD based on maternal factors. The developed model provided insights into the maternal emotion and nutrition factors associated with ASD and highlighted the potential clinical applicability of the developed model in identifying high-risk populations.

Residual graph transformer for autism spectrum disorder prediction

Brain functional connectivity (FC) based on resting-state functional magnetic resonance imaging (rs-fMRI) has been in vogue to predict Autism Spectrum Disorder (ASD), which is a neuropsychiatric disease up the plight of locating latent biomarkers for clinical diagnosis. Albeit massive endeavors have been made, most studies are fed up with several chronic issues, such as the intractability of harnessing the interaction flourishing within brain regions, the astriction of representation due to vanishing gradient within deeper network architecture, and the poor interpretability leading to unpersuasive diagnosis. To ameliorate these issues, a FC-learned Residual Graph Transformer Network, namely RGTNet, is proposed. Specifically, we design a Graph Encoder to extract temporal-related features with long-range dependencies, from which interpretable FC matrices would be modeled. Besides, the residual trick is introduced to deepen the GCN architecture, thereby learning the higher-level information. Moreover, a novel Graph Sparse Fitting followed by weighted aggregation is proposed to ease dimensionality explosion. Empirically, the results on two types of ABIDE data sets demonstrate the meliority of RGTNet. Notably, the achieved ACC metric reaches 73.4%, overwhelming most competitors with merely 70.9% on the AAL atlas using a five-fold cross-validation policy. Moreover, the investigated biomarkers concord closely with the authoritative medical knowledge, paving a viable way for ASD-clinical diagnosis. Our code is available at https://github.com/CodeGoat24/RGTNet.

Using visual attention estimation on videos for automated prediction of autism spectrum disorder and symptom severity in preschool children.

Atypical visual attention in individuals with autism spectrum disorders (ASD) has been utilised as a unique diagnosis criterion in previous research. This paper presents a novel approach to the automatic and quantitative screening of ASD as well as symptom severity prediction in preschool children. We develop a novel computational pipeline that extracts learned features from a dynamic visual stimulus to classify ASD children and predict the level of ASD-related symptoms. Experimental results demonstrate promising performance that is superior to using handcrafted features and machine learning algorithms, in terms of evaluation metrics used in diagnostic tests. Using a leave-one-out cross-validation approach, we obtained an accuracy of 94.59%, a sensitivity of 100%, a specificity of 76.47% and an area under the receiver operating characteristic curve (AUC) of 96% for ASD classification. In addition, we obtained an accuracy of 94.74%, a sensitivity of 87.50%, a specificity of 100% and an AUC of 99% for ASD symptom severity prediction.

Prediction of Autism Spectrum Disorder: Comparison and Tuning of Machine Learning Models

The early diagnosis in Autism Spectrum Disorder (ASD) is crucial for timely interventions to address the patients attentional and social challenges. The currently study aims to use machine learning algorithms to accurately predict ASD outcomes. Dataset from a Kaggle competition was used to perform the prediction analysis. Five supervised machine learning algorithms were employed: Logistic Regression (LR), K-Nearest Neighbors (KNN), Support Vector Machine Classifier (SVC), Random Forest (RF), and Decision Trees (DT). The models were fine-tuned using a range of possible hyperparameters and evaluated using ROC AUC scores. The best-performing model, Random Forest, achieved a training ROC AUC of 0.93. The model's performance in predicting the unseen test set resulted in a ROC AUC score of 0.8623. The outcome demonstrates the potentials of machine learning models in early prediction of ASD symptoms, which provides support for autistic individuals to enhance their quality of life and education.

A Comprehensive Study of Machine Learning Algorithms to Predict Autism Spectrum Disorder (ASD).

A Comprehensive Study of Machine Learning Algorithms to Predict Autism Spectrum Disorder (ASD).

On effectively predicting autism spectrum disorder therapy using an ensemble of classifiers

An ensemble of classifiers combines several single classifiers to deliver a final prediction or classification decision. An increasingly provoking question is whether such an ensemble can outperform the single best classifier. If so, what form of ensemble learning system (also known as multiple classifier learning systems) yields the most significant benefits in the size or diversity of the ensemble? In this paper, the ability of ensemble learning to predict and identify factors that influence or contribute to autism spectrum disorder therapy (ASDT) for intervention purposes is investigated. Given that most interventions are typically short-term in nature, henceforth, developing a robotic system that will provide the best outcome and measurement of ASDT therapy has never been so critical. In this paper, the performance of five single classifiers against several multiple classifier learning systems in exploring and predicting ASDT is investigated using a dataset of behavioural data and robot-enhanced therapy against standard human treatment based on 3000 sessions and 300 h, recorded from 61 autistic children. Experimental results show statistically significant differences in performance among the single classifiers for ASDT prediction with decision trees as the more accurate classifier. The results further show multiple classifier learning systems (MCLS) achieving better performance for ASDT prediction (especially those ensembles with three core classifiers). Additionally, the results show bagging and boosting ensemble learning as robust when predicting ASDT with multi-stage design as the most dominant architecture. It also appears that eye contact and social interaction are the most critical contributing factors to the ASDT problem among children.

A Comparative Study: Deep Learning Approach to Predict Autism Spectrum Disorder Based on Q-Chat-10 Responses

Autism spectrum disorder (ASD) is a neurodevelopmental condition that has been observed to have an increasing incidence and significant health-related expenses. The timely identification of these burdens can mitigate their impact; however, current diagnostic protocols are protracted and entail significant expenses. The implementation of machine learning and, more recently, deep learning techniques presents promising remedies to improve ASD screening procedures. The present research introduces a deep learning framework for the purpose of forecasting autism spectrum disorder (ASD) utilizing responses obtained from the Q-Chat-10 questionnaire. The dataset employed in this study comprises 1054 records, encompassing ten behavioral traits and additional individual characteristics. The objective of this study is to improve the precision, efficacy, sensitivity, and specificity of autism spectrum disorder (ASD) predictions by contrasting the performance of a deep learning model with that of conventional machine learning models. The implementation of this technology has the potential to significantly optimize the ASD screening procedure, rendering it more affordable and convenient and ultimately assisting healthcare practitioners in their clinical judgment for prompt ASD identification.

Factors in Infancy That May Predict Autism Spectrum Disorder.

The global increase in the prevalence of ASD (Autism Spectrum Disorder) is of great medical importance, but the reasons for this increase are still unknown. This study sought to identify possible early contributing factors in children who were later diagnosed with ASD. In this retrospective cohort study, postnatal records of 1105 children diagnosed with ASD were analyzed to determine if any signs of ASD could be found in a large database of births and well-baby care programs. We compared the recordings of typically developing children and analyzed the differences statistically. Rapid increases in weight, height, and head circumference during early infancy predict the development of ASD. In addition, low birth weight, older maternal age, and increased weight and height percentiles at six months of age together predict the development of ASD. At two years of age, these four parameters, in addition to impaired motor development, can also predict the development of ASD. These results suggest that the recent increase in ASD prevalence is associated with the "obesity epidemic" and with recommendations of supine sleeping to prevent Sudden Infant Death Syndrome, associated with atypical neural network development in the brain.