An Integrated Statistical and Clinically Applicable Machine Learning Framework for the Detection of Autism Spectrum Disorder

Uddin, Md. Jamal; Ahamad, Md. Martuza; Sarker, Prodip Kumar; Aktar, Sakifa; Alotaibi, Naif; Alyami, Salem A.; Kabir, Muhammad Ashad; Moni, Mohammad Ali

doi:10.3390/computers12050092

Cited by 13 publications

(3 citation statements)

References 41 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…XGBoost includes regularization techniques to prevent overfitting, which is a common issue in complex models. Regularization penalizes overly complex models to ensure they do not fit noise in the training data, leading to better generalization on unseen data [ 40 ]. XGBoost’s objective function measures the model’s performance during training.…”

Section: Methodsmentioning

confidence: 99%

Hybrid Techniques of Facial Feature Image Analysis for Early Detection of Autism Spectrum Disorder Based on Combined CNN Features

Awaji,

Senan,

Olayah

et al. 2023

Diagnostics

View full text Add to dashboard Cite

Autism spectrum disorder (ASD) is a complex neurodevelopmental disorder characterized by difficulties in social communication and repetitive behaviors. The exact causes of ASD remain elusive and likely involve a combination of genetic, environmental, and neurobiological factors. Doctors often face challenges in accurately identifying ASD early due to its complex and diverse presentation. Early detection and intervention are crucial for improving outcomes for individuals with ASD. Early diagnosis allows for timely access to appropriate interventions, leading to better social and communication skills development. Artificial intelligence techniques, particularly facial feature extraction using machine learning algorithms, display promise in aiding the early detection of ASD. By analyzing facial expressions and subtle cues, AI models identify patterns associated with ASD features. This study developed various hybrid systems to diagnose facial feature images for an ASD dataset by combining convolutional neural network (CNN) features. The first approach utilized pre-trained VGG16, ResNet101, and MobileNet models. The second approach employed a hybrid technique that combined CNN models (VGG16, ResNet101, and MobileNet) with XGBoost and RF algorithms. The third strategy involved diagnosing ASD using XGBoost and an RF based on features of VGG-16-ResNet101, ResNet101-MobileNet, and VGG16-MobileNet models. Notably, the hybrid RF algorithm that utilized features from the VGG16-MobileNet models demonstrated superior performance, reached an AUC of 99.25%, an accuracy of 98.8%, a precision of 98.9%, a sensitivity of 99%, and a specificity of 99.1%.

show abstract

Section: Methodsmentioning

confidence: 99%

Hybrid Techniques of Facial Feature Image Analysis for Early Detection of Autism Spectrum Disorder Based on Combined CNN Features

Awaji,

Senan,

Olayah

et al. 2023

Diagnostics

View full text Add to dashboard Cite

show abstract

“…Gathering the early detection data of children, adults, toddlers and adolescents, they transformed and applied several ML techniques for classification. Uddin et al [38] developed an ML-based framework considering the ASD screening dataset for toddlers for the detection of ASD. They applied the SMOTE method to balance the dataset and applied several ML techniques among which AdaBoost got the highest accuracy.…”

Section: Introductionmentioning

confidence: 99%

ASDNet: A robust involution‐based architecture for diagnosis of autism spectrum disorder utilising eye‐tracking technology

Mumenin,

Yousuf,

Nashiry

et al. 2024

IET Computer Vision

Self Cite

View full text Add to dashboard Cite

Autism Spectrum Disorder (ASD) is a chronic condition characterised by impairments in social interaction and communication. Early detection of ASD is desired, and there exists a demand for the development of diagnostic aids to facilitate this. A lightweight Involutional Neural Network (INN) architecture has been developed to diagnose ASD. The model follows a simpler architectural design and has less number of parameters than the state‐of‐the‐art (SOTA) image classification models, requiring lower computational resources. The proposed model is trained to detect ASD from eye‐tracking scanpath (SP), heatmap (HM), and fixation map (FM) images. Monte Carlo Dropout has been applied to the model to perform an uncertainty analysis and ensure the effectiveness of the output provided by the proposed INN model. The model has been trained and evaluated using two publicly accessible datasets. From the experiment, it is seen that the model has achieved 98.12% accuracy, 96.83% accuracy, and 97.61% accuracy on SP, FM, and HM, respectively, which outperforms the current SOTA image classification models and other existing works conducted on this topic.

show abstract

“…Machine learning (ML) methods have been used to solve several problems recently, such as diagnosing cancer [9], COVID-19 [10], autism [11,12], meningitis, diabetes, and heart disease. Recent research suggests that ML can summarize patient characteristics and predict T2DM risk [13][14][15][16][17].…”

Section: Introductionmentioning

confidence: 99%

A Comparison of Machine Learning Techniques for the Detection of Type-2 Diabetes Mellitus: Experiences from Bangladesh

et al. 2023

Self Cite

View full text Add to dashboard Cite

Diabetes is a chronic disease caused by a persistently high blood sugar level, causing other chronic diseases, including cardiovascular, kidney, eye, and nerve damage. Prompt detection plays a vital role in reducing the risk and severity associated with diabetes, and identifying key risk factors can help individuals become more mindful of their lifestyles. In this study, we conducted a questionnaire-based survey utilizing standard diabetes risk variables to examine the prevalence of diabetes in Bangladesh. To enable prompt detection of diabetes, we compared different machine learning techniques and proposed an ensemble-based machine learning framework that incorporated algorithms such as decision tree, random forest, and extreme gradient boost algorithms. In order to address class imbalance within the dataset, we initially applied the synthetic minority oversampling technique (SMOTE) and random oversampling (ROS) techniques. We evaluated the performance of various classifiers, including decision tree (DT), logistic regression (LR), support vector machine (SVM), gradient boost (GB), extreme gradient boost (XGBoost), random forest (RF), and ensemble technique (ET), on our diabetes datasets. Our experimental results showed that the ET outperformed other classifiers; to further enhance its effectiveness, we fine-tuned and evaluated the hyperparameters of the ET. Using statistical and machine learning techniques, we also ranked features and identified that age, extreme thirst, and diabetes in the family are significant features that prove instrumental in the detection of diabetes patients. This method has great potential for clinicians to effectively identify individuals at risk of diabetes, facilitating timely intervention and care.

show abstract

An Integrated Statistical and Clinically Applicable Machine Learning Framework for the Detection of Autism Spectrum Disorder

Cited by 13 publications

References 41 publications

Hybrid Techniques of Facial Feature Image Analysis for Early Detection of Autism Spectrum Disorder Based on Combined CNN Features

Hybrid Techniques of Facial Feature Image Analysis for Early Detection of Autism Spectrum Disorder Based on Combined CNN Features

ASDNet: A robust involution‐based architecture for diagnosis of autism spectrum disorder utilising eye‐tracking technology

A Comparison of Machine Learning Techniques for the Detection of Type-2 Diabetes Mellitus: Experiences from Bangladesh

Contact Info

Product

Resources

About