Efficient Diagnosis of Autism with Optimized Machine Learning Models: An Experimental Analysis on Genetic and Personal Characteristic Datasets

Alsuliman, Maraheb; Al-Baity, Heyam H.

doi:10.3390/app12083812

Cited by 8 publications

(4 citation statements)

References 31 publications

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“…A few studies have yielded promising results [29] using advanced technologies like Boruta or bio-inspired algorithms like the GWO and particle swarm optimization (PSO). For example, four bio-inspired methods were used in [30] to create optimized ML models using gene expression data to predict ASD. In [30], four bio-inspired methods were employed to develop optimized ML models using gene expression data for ASD prediction.…”

Section: Related Workmentioning

confidence: 99%

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Efficient Diagnosis of Autism Spectrum Disorder Using Optimized Machine Learning Models Based on Structural MRI

Bahathiq,

Banjar,

Jarraya

et al. 2024

Applied Sciences

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Autism spectrum disorder (ASD) affects approximately 1.4% of the population and imposes significant social and economic burdens. Because its etiology is unknown, effective diagnosis is challenging. Advancements in structural magnetic resonance imaging (sMRI) allow for the objective assessment of ASD by examining structural brain changes. Recently, machine learning (ML)-based diagnostic systems have emerged to expedite and enhance the diagnostic process. However, the expected success in ASD was not yet achieved. This study evaluates and compares the performance of seven optimized ML models to identify sMRI-based biomarkers for early and accurate detection of ASD in children aged 5 to 10 years. The effect of using hyperparameter tuning and feature selection techniques are investigated using two public datasets from Autism Brain Imaging Data Exchange Initiative. Furthermore, these models are tested on a local Saudi dataset to verify their generalizability. The integration of the grey wolf optimizer with a support vector machine achieved the best performance with an average accuracy of 71% (with further improvement to 71% after adding personal features) using 10-fold Cross-validation. The optimized models identified relevant biomarkers for diagnosis, lending credence to their truly generalizable nature and advancing scientific understanding of neurological changes in ASD.

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Section: Related Workmentioning

confidence: 99%

“…For example, four bio-inspired methods were used in [30] to create optimized ML models using gene expression data to predict ASD. In [30], four bio-inspired methods were employed to develop optimized ML models using gene expression data for ASD prediction. The GWO-SVM model achieved an accuracy of 99%.…”

Section: Related Workmentioning

confidence: 99%

Efficient Diagnosis of Autism Spectrum Disorder Using Optimized Machine Learning Models Based on Structural MRI

Bahathiq,

Banjar,

Jarraya

et al. 2024

Applied Sciences

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“…The authors in [31] performed an evolutionary cultural optimization algorithm to optimize the weights of Artificial Neural Networks (ANN) in classifying three benchmark datasets of autism screening Toddlers, Children, and Adults. The authors in [32] performed an experimental analysis using 16 different ML models, among them, four bio-inspired algorithms, namely, Gray Wolf Optimization (GWO), Flower Pollination Algorithm (FPA), Bat Algorithms (BA), and Artificial Bee Colony (ABC) were employed for optimizing the wrapper feature selection method in order to select the most informative features and to increase the accuracy of the classification models on genetic and personal characteristics datasets. Another study conducted by the authors in [33] combined three benchmark datasets as Toddlers, Adolescents, and Adults and performed a Light Gradient Boosting Machine (LGBM) classifier to classify ASD.…”

Section: Introductionmentioning

confidence: 99%

A Machine Learning Framework for Early-Stage Detection of Autism Spectrum Disorders

et al. 2023

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Autism Spectrum Disorder (ASD) is a type of neurodevelopmental disorder that affects the everyday life of affected patients. Though it is considered hard to completely eradicate this disease, disease severity can be mitigated by taking early interventions. In this paper, we propose an effective framework for the evaluation of various Machine Learning (ML) techniques for the early detection of ASD. The proposed framework employs four different Feature Scaling (FS) strategies i.e., Quantile Transformer (QT), Power Transformer (PT), Normalizer, and Max Abs Scaler (MAS). Then, the feature-scaled datasets are classified through eight simple but effective ML algorithms like Ada Boost (AB), Random Forest (RF), Decision Tree (DT), K-Nearest Neighbors (KNN), Gaussian Naïve Bayes (GNB), Logistic Regression (LR), Support Vector Machine (SVM) and Linear Discriminant Analysis (LDA). Our experiments are performed on four standard ASD datasets (Toddlers, Adolescents, Children, and Adults). Comparing the classification outcomes using various statistical evaluation measures (Accuracy, Receiver Operating Characteristic: ROC curve, F1-score, Precision, Recall, Mathews Correlation Coefficient: MCC, Kappa score, and Log loss), the best-performing classification methods, and the best FS techniques for each ASD dataset are identified. After analyzing the experimental outcomes of different classifiers on feature-scaled ASD datasets, it is found that AB predicted ASD with the highest accuracy of 99.25%, and 97.95% for Toddlers and Children, respectively and LDA predicted ASD with the highest accuracy of 97.12% and 99.03% for Adolescents and Adults datasets, respectively. These highest accuracies are achieved while scaling Toddlers and Children with normalizer FS and Adolescents and Adults with the QT FS method. Afterward, the ASD risk factors are calculated, and the most important attributes are ranked according to their importance values using four different Feature Selection Techniques (FSTs) i.e., Info Gain Attribute Evaluator (IGAE), Gain Ratio Attribute Evaluator (GRAE), Relief F Attribute Evaluator (RFAE), and Correlation Attribute Evaluator (CAE). These detailed experimental evaluations indicate that proper finetuning of the ML methods can play an essential role in predicting ASD in people of different ages. We argue that the detailed feature importance analysis in this paper will guide the decision-making of healthcare practitioners while screening ASD cases. The proposed framework has achieved promising results compared to existing approaches for the early detection of ASD.

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“…Studies suggest that autism rates among children have tripled from 2000 to 2016 in the New York metropolitan area [2]. In 2018, 1 in 44 children was diagnosed with ASD [3]. In the past, several studies have been carried out to either analyze autism in individuals or to provide a potential solution for combating the disorder.…”

Section: Introductionmentioning

confidence: 99%

Autism Screening in Toddlers and Adults Using Deep Learning and Fair AI Techniques

Priyadarshini

2023

Future Internet

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Autism spectrum disorder (ASD) has been associated with conditions like depression, anxiety, epilepsy, etc., due to its impact on an individual’s educational, social, and employment. Since diagnosis is challenging and there is no cure, the goal is to maximize an individual’s ability by reducing the symptoms, and early diagnosis plays a role in improving behavior and language development. In this paper, an autism screening analysis for toddlers and adults has been performed using fair AI (feature engineering, SMOTE, optimizations, etc.) and deep learning methods. The analysis considers traditional deep learning methods like Multilayer Perceptron (MLP), Artificial Neural Networks (ANN), Convolutional Neural Networks (CNN), and Long Short-Term Memory (LSTM), and also proposes two hybrid deep learning models, i.e., CNN–LSTM with Particle Swarm Optimization (PSO), and a CNN model combined with Gated Recurrent Units (GRU–CNN). The models have been validated using multiple performance metrics, and the analysis confirms that the proposed models perform better than the traditional models.

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Efficient Diagnosis of Autism with Optimized Machine Learning Models: An Experimental Analysis on Genetic and Personal Characteristic Datasets

Cited by 8 publications

References 31 publications

Efficient Diagnosis of Autism Spectrum Disorder Using Optimized Machine Learning Models Based on Structural MRI

Efficient Diagnosis of Autism Spectrum Disorder Using Optimized Machine Learning Models Based on Structural MRI

A Machine Learning Framework for Early-Stage Detection of Autism Spectrum Disorders

Autism Screening in Toddlers and Adults Using Deep Learning and Fair AI Techniques

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