BackgroundAutism spectrum disorder (ASD) is a neurodevelopmental disease marked by a variety of repetitive behaviors and social communication difficulties.PurposeTo develop a generalizable machine learning (ML) classifier that can accurately and effectively predict ASD in children.Material and MethodsThis paper makes use of neuroimaging data from the Autism Brain Imaging Data Exchange (ABIDE I and II) datasets through a combination of structural and functional magnetic resonance imaging data. Several ML models, such as Support Vector Machines (SVM), CatBoost, random forest (RF), and stack classifiers, were tested to demonstrate which model performs the best in ASD classification when used alongside a deep convolutional neural network.ResultsResults showed that stack classifier performed the best among the models, with the highest accuracy of 81.68%, sensitivity of 85.08%, and specificity of 79.13% for ABIDE I, and 81.34%, 83.61%, and 82.21% for ABIDE II, showing its superior ability to identify complex patterns in neuroimaging data. SVM performed poorly across all metrics, showing its limitations in dealing with high-dimensional neuroimaging data.ConclusionThe results show that the application of ML models, especially ensemble approaches like stack classifier, holds significant promise in improving the accuracy with which ASD is detected using neuroimaging and thus shows their potential for use in clinical applications and early intervention strategies.

Autism spectrum disorder (ASD) includes a varied set of neuropsychiatric illnesses. This disorder is described by a definite grade of loss in social communication, academic functioning, personal contact, and limited and repetitive behaviours. Individuals with ASD might perform, convey, and study in a different way than others. ASDs naturally are apparent before age 3 years, with related impairments affecting manifold regions of a person's lifespan. Deep learning (DL) and machine learning (ML) techniques are used in medical research to diagnose and detect ASD promptly. This study presents a Two-Tier Metaheuristic-Driven Ensemble Deep Learning for Effective Autism Spectrum Disorder Diagnosis in Disabled Persons (T2MEDL-EASDDP) model. The main aim of the presented T2MEDL-EASDDP model is to analyze and diagnose the different stages of ASD in disabled individuals. To accomplish this, the T2MEDL-EASDDP model utilizes min-max normalization for data pre-processing to ensure that the input data is scaled to a uniform range. Furthermore, the improved butterfly optimization algorithm (IBOA)-based feature selection (FS) is utilized to identify the most relevant features and reduce dimensionality efficiently. Additionally, an ensemble of DL holds three approaches, namely autoencoder (AE), long short-term memory (LSTM), and deep belief network (DBN) approach is employed for analyzing and detecting ASD. Finally, the presented T2MEDL-EASDDP model employs brownian motion (BM) and directional mutation scheme-based coati optimizer algorithm (BDCOA) techniques to fine-tune the hyperparameters involved in the three ensemble methods. A wide range of simulation analyses of the T2MEDL-EASDDP technique is accomplished under the ASD-Toddler and ASD-Adult datasets. The performance validation of the T2MEDL-EASDDP method portrayed a superior accuracy value of 97.79% over existing techniques.

Social communication difficulties are a characteristic of autism spectrum disorder (ASD), a neurodevelopmental condition. The earlier method of diagnosing autism largely relied on error-prone behavioral observation of symptoms. More intelligence approaches are in progress to diagnose the disorder, which still demands improvement in prediction accuracy. Furthermore, computer-aided design systems based on machine learning algorithms are extremely time-consuming and difficult to design. This study used deep learning techniques to develop a novel autism detection model in order to overcome these problems.

Preprocessing, Features extraction, Improved Feature level Fusion, and Detection are the phases of the suggested autism detection methodology. First, both input modalities will be preprocessed so they are ready for the next stages to be processed. In this case, the facial picture is preprocessed utilizing the Gabor filtering technique, while the input EEG data is preprocessed through Wiener filtering. Subsequently, features are extracted from the modalities, from the EEG signal data, features like Common Spatial Pattern (CSP), Improved Singular Spectrum Entropy, and correlation dimension, are extracted. From the face image, features like the Improved Active Appearance model, Gray-Level Co-occurrence matrix (GLCM) features and Proposed Shape Local Binary Texture (SLBT), as well are retrieved. Following extraction, enhanced feature-level fusion is performed to fuse the features. Ultimately, the combined features are fed into the hybrid model to complete the diagnosis. Models such as Convolutional Neural Networks (CNN) and Bidirectional Gated Recurrent Units (Bi-GRU) are part of the hybrid model.

The suggested MADDHM model achieved an accuracy of about 91.03 % regarding EEG and 91.67 % regarding face analysis meanwhile, SVM=87.49 %, DNN=88.59 %, Bi-GRU=90.02 %, LSTM=87.49 % and CNN=82.02 %.

As a result, the suggested methodology provides encouraging outcomes and opens up possibilities for early autism detection. The development of such models is not only a technical achievement but also a step forward in providing timely interventions for individuals with ASD.

Autism spectrum disorder (ASD) is a neurodevelopmental disorder. ASD cannot be fully cured, but early-stage diagnosis followed by therapies and rehabilitation helps an autistic person to live a quality life. Clinical diagnosis of ASD symptoms via questionnaire and screening tests such as Autism Spectrum Quotient-10 (AQ-10) and Quantitative Check-list for Autism in Toddlers (Q-chat) are expensive, inaccessible, and time-consuming processes. Machine learning (ML) techniques are beneficial to predict ASD easily at the initial stage of diagnosis. The main aim of this work is to classify ASD and typical developed (TD) class data using ML classifiers. In our work, we have used different ASD data sets of all age groups (toddlers, adults, children, and adolescents) to classify ASD and TD cases. We implemented One-Hot encoding to translate categorical data into numerical data during preprocessing. We then used kNN Imputer with MinMaxScaler feature transformation to handle missing values and data normalization. ASD and TD class data is classified using Support vector machine, k-nearest-neighbor (KNN), random forest (RF), and artificial neural network classifiers. RF gives the best performance in terms of the accuracy of 100% with different training and testing data split for all four types of data sets and has no over-fitting issue. We have also examined our results with already published work, including recent methods like Deep Neural Network (DNN) and Convolution Neural Network (CNN). Even using complex architectures like DNN and CNN, our proposed methods provide the best results with low-complexity models. In contrast, existing methods have shown accuracy upto 98% with log-loss upto 15%. Our proposed methodology demonstrates the improved generalization for real-time ASD detection during clinical trials.

Early identification and intervention often leads to improved life outcomes for individuals with Autism Spectrum Disorder (ASD). However, traditional diagnostic methods are time-consuming, frequently delaying treatment. This study examines the application of machine learning (ML) techniques to 10-question Quantitative Checklist for Autism in Toddlers (QCHAT-10) datasets, aiming to evaluate the predictive value of questionnaire features and overall accuracy metrics across different cultures. We trained models using three distinct datasets from three different countries: Poland, New Zealand, and Saudi Arabia. The New Zealand and Saudi Arabian-trained models were both tested on the Polish dataset, which consisted of diagnostic class labels derived from clinical diagnostic processes. The Decision Tree, Random Forest, and XGBoost models were evaluated, with XGBoost consistently performing best. Feature importance rankings revealed little consistency across models; however, Recursive Feature Elimination (RFE) to select the models with the four most predictive features retained three common features. Both models performed similarly on the Polish test dataset with clinical diagnostic labels, with the New Zealand models with all 13 features achieving an AUROC of 0.94 ± 0.06, and the Saudi Model having an AUROC of 93% ± 6. This compared favorably to the cross-validation analysis of a Polish-trained model, which had an AUROC of 94% ± 5, suggesting that answers to the QCHAT-10 can be predictive of an official autism diagnosis, even across cultures. The New Zealand model with four features had an AUROC of 85% ± 13, and the Saudi model had a similar result of 87% ± 11. These results were somewhat lower than the Polish cross-validation AUROC of 91% ± 5. Adjusting probability thresholds improved sensitivity in some models, which is crucial for screening tools. However, this threshold adjustment often resulted in low levels of specificity during the final testing phase. Our findings suggest that these screening tools may generalize well across cultures; however, more research is needed regarding differences in feature importance for different populations.

Artificial intelligence (AI) methods attempt to simulate the behavior and the neural activity of the brain. In particular, Convolutional Neural Networks (CNNs) offer state-of-the-art models of the ventral visual stream. Furthermore, no proposed model estimates the distance between objects as a function of the dorsal stream. In this paper, we present a quantitatively accurate model for the visual system. Specifically, we propose a VeDo-Net model that comprises both ventral and dorsal branches. As in the ventral visual stream, our model recognizes objects. The model also locates and estimates the distance between objects as a spatial relationship task performed by the dorsal stream. One application of the proposed model is in the simulation of visual impairments. In this study, however, we show how the proposed model can simulate the occurrence of dorsal stream impairments such as Autism Spectrum Disorder (ASD) and cerebral visual impairment (CVI). In the end, we explore the impacts of learning on the recovery of the synaptic disruptions of the dorsal visual stream. Results indicated a direct relationship between the positive and negative changes in the weights of the dorsal stream's last layers and the output of the dorsal stream under an allocentric situation. Our results also demonstrate that visual-spatial perception impairments in ASD may be caused by a disturbance in the last layers of the dorsal stream.

Autism spectrum disorder (ASD) is a neurodevelopmental condition that significantly impacts the mental, emotional, and social development of children. Early screening for ASD typically involves the use of a series of questionnaires. With answers to these questionnaires, healthcare professionals can identify whether a child is at risk for developing ASD and refer them for further evaluation and diagnosis. CHAT-23 is an effective and widely used screening test in China for the early screening of ASD, which contains 23 different kinds of questions.

We have collected clinical data from Wuxi, China. All the questions of CHAT-23 are regarded as different kinds of features for building machine learning models. We introduce machine learning methods into ASD screening, using the Max-Relevance and Min-Redundancy (mRMR) feature selection method to analyze the most important questions among all 23 from the collected CHAT-23 questionnaires. Seven mainstream supervised machine learning models were built and experiments were conducted.

Among the seven supervised machine learning models evaluated, the best-performing model achieved a sensitivity of 0.909 and a specificity of 0.922 when the number of features was reduced to 9. This demonstrates the model's ability to accurately identify children for ASD with high precision, even with a more concise set of features.

Our study focuses on the health of Chinese children, introducing machine learning methods to provide more accurate and effective early screening tests for autism. This approach not only enhances the early detection of ASD but also helps in refining the CHAT-23 questionnaire by identifying the most relevant questions for the diagnosis process.

With the increasing prevalence of autism spectrum disorders (ASD), the importance of early screening and diagnosis has been subject to considerable discussion. Given the subtle differences between ASD children and typically developing children during the early stages of development, it is imperative to investigate the utilization of automatic recognition methods powered by artificial intelligence. We aim to summarize the research work on this topic and sort out the markers that can be used for identification.

We searched the papers published in the Web of Science, PubMed, Scopus, Medline, SpringerLink, Wiley Online Library, and EBSCO databases from 1st January 2013 to 13th November 2023, and 43 articles were included.

These articles mainly divided recognition markers into five categories: gaze behaviors, facial expressions, motor movements, voice features, and task performance. Based on the above markers, the accuracy of artificial intelligence screening ranged from 62.13 to 100%, the sensitivity ranged from 69.67 to 100%, the specificity ranged from 54 to 100%.

Therefore, artificial intelligence recognition holds promise as a tool for identifying children with ASD. However, it still needs to continually enhance the screening model and improve accuracy through multimodal screening, thereby facilitating timely intervention and treatment.

Fetal alcohol spectrum disorders include a variety of physical and neurocognitive disorders caused by prenatal alcohol exposure. Although their overall prevalence is around 0.77%, FASD remains underdiagnosed and little known, partly due to the complexity of their diagnosis, which shares some symptoms with other pathologies such as autism spectrum, depression or hyperactivity disorders.

This study included 73 control and 158 patients diagnosed with FASD. Variables selected were based on IOM classification from 2016, including sociodemographic, clinical, and psychological characteristics. Statistical analysis included Kruskal-Wallis test for quantitative factors, Chi-square test for qualitative variables, and Machine Learning (ML) algorithms for predictions.

This study explores the application ML in diagnosing FASD and its subtypes: Fetal Alcohol Syndrome (FAS), partial FAS (pFAS), and Alcohol-Related Neurodevelopmental Disorder (ARND). ML constructed a profile for FASD based on socio-demographic, clinical, and psychological data from children with FASD compared to a control group. Random Forest (RF) model was the most efficient for predicting FASD, achieving the highest metrics in accuracy (0.92), precision (0.96), sensitivity (0.92), F1 Score (0.94), specificity (0.92), and AUC (0.92). For FAS, XGBoost model obtained the highest accuracy (0.94), precision (0.91), sensitivity (0.91), F1 Score (0.91), specificity (0.96), and AUC (0.93). In the case of pFAS, RF model showed its effectiveness, with high levels of accuracy (0.90), precision (0.86), sensitivity (0.96), F1 Score (0.91), specificity (0.83), and AUC (0.90). For ARND, RF model obtained the best levels of accuracy (0.87), precision (0.76), sensitivity (0.93), F1 Score (0.84), specificity (0.83), and AUC (0.88). Our study identified key variables for efficient FASD screening, including traditional clinical characteristics like maternal alcohol consumption, lip-philtrum, microcephaly, height and weight impairment, as well as neuropsychological variables such as the Working Memory Index (WMI), aggressive behavior, IQ, somatic complaints, and depressive problems.

Our findings emphasize the importance of ML analyses for early diagnoses of FASD, allowing a better understanding of FASD subtypes to potentially improve clinical practice and avoid misdiagnosis.

Autism Spectrum Disorder (ASD) is a neurodevelopmental condition that is characterized by communication barriers, societal disengagement, and monotonous actions. Currently, the diagnosis of ASD is made by experts through a subjective and time-consuming qualitative behavioural examination using internationally recognized descriptive standards. In this paper, we present an EEG-based three-phase novel approach comprising 29 autistic subjects and 30 neurotypical people. In the first phase, preprocessing of data is performed from which we derived one continuous dataset and four condition-based datasets to determine the role of each dataset in the identification of autism from neurotypical people. In the second phase, time-domain and morphological features were extracted and four different feature selection techniques were applied. In the last phase, five-fold cross-validation is used to evaluate six different machine learning models based on the performance metrics and computational efficiency. The neural network outperformed when trained with maximum relevance and minimum redundancy (MRMR) algorithm on the continuous dataset with 98.10% validation accuracy and 0.9994 area under the curve (AUC) value for model validation, and 98.43% testing accuracy and AUC test value of 0.9998. The decision tree overall performed the second best in terms of computational efficiency and performance accuracy. The results indicate that EEG-based machine learning models have the potential for ASD identification from neurotypical people with a more objective and reliable method.

ASD (autism spectrum disorder) is a complex developmental and neurological disorder that impacts the social life of the affected person by disturbing their capability for interaction and communication. As it is a behavioural disorder, early treatment will improve the quality of life of ASD patients. Traditional screening is carried out with behavioural assessment through trained physicians, which is expensive and time-consuming. To resolve the issue, several conventional methods strive to achieve an effective ASD identification system, but are limited by handling large data sets, accuracy, and speed. Therefore, the proposed identification system employed the MBA (modified bat) algorithm based on ANN (artificial neural networks), modified ANN (modified artificial neural networks), DT (decision tree), and KNN (k-nearest neighbours) for the classification of ASD in children and adolescents. A BA (bat algorithm) is utilised for the automatic zooming capability, which improves the system's efficacy by excellently finding the solutions in the identification system. Conversely, BA is effective in the identification, it still has certain drawbacks like speed, accuracy, and falls into local extremum. Therefore, the proposed identification system modifies the BA optimisation with random perturbation of trends and optimal orientation. The dataset utilised in the respective model is the Q-chat-10 dataset. This dataset contains data of four stages of age groups such as toddlers, children, adolescents, and adults. To analyse the quality of the dataset, dataset evaluation mechanism, such as the Chi-Squared Statistic and p-value, are used in the respective research. The evaluation signifies the relation of the dataset with respect to the proposed model. Further, the performance of the proposed detection system is examined with certain performance metrics to calculate its efficiency. The outcome revealed that the modified ANN classifier model attained an accuracy of 1.00, ensuring improved performance when compared with other state-of-the-art methods. Thus, the proposed model was intended to assist physicians and researchers in enhancing the diagnosis of ASD to improve the standard of life of ASD patients.

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.

Autism spectrum disorder (ASD) presents a neurological and developmental disorder that has an impact on the social and cognitive skills of children causing repetitive behaviours, restricted interests, communication problems and difficulty in social interaction. Early diagnosis of ASD can prevent from its severity and prolonged effects. Federated learning (FL) is one of the most recent techniques that can be applied for accurate ASD diagnoses in early stages or prevention of its long-term effects. In this article, FL technique has been uniquely applied for autism detection by training two different ML classifiers including logistic regression and support vector machine locally for classification of ASD factors and detection of ASD in children and adults. Due to FL, results obtained from these classifiers have been transmitted to central server where meta classifier is trained to determine which approach is most accurate in the detection of ASD in children and adults. Four different ASD patient datasets, each containing more than 600 records of effected children and adults have been obtained from different repository for features extraction. The proposed model predicted ASD with 98% accuracy (in children) and 81% accuracy (in adults).