Concatenating framework in ASD analysis towards research progress

Roopa, B. S.; Prasad, Raghuram

doi:10.1109/icatiece45860.2019.9063816

Cited by 6 publications

(2 citation statements)

References 10 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Para isso eles mesmos selecionaram crianças, cujo diagnóstico do TEA já tivesse sido realizado, e aplicaram algoritmos para detalhar as características faciais de cada indivíduo criando, assim, uma BD. Após esse processo, os dados foram submetidos a três algoritmos de AM, sendo eles: SVM, RF e Neural Networks Multilayer Perceptron (MLP), descrevendo dois cenários distintos, onde o primeiro cenário foram empregadas [32] Genéticos SVM, Naive Bayes (NB), LDA, KNN Liu et al (2020) [33] Imagens ressônancia magnética SVM Elnakieb et al (2020) [34] Imagens ressônancia magnética SVM, DT, KNN, Neural Network (NN) Huang et al (2019) [35] Imagens ressônancia magnética SVM ElNakieb et al (2019) [36] Imagens ressônancia magnética SVM, KNN, DT, NN, Deep Neural Network (DNN) Mostafa, Tang and Wu (2019) [37] Imagens ressônancia magnética LDA, Logistic Regression (LR), SVM, KNN, NN Haputhanthri et al (2019) [38] Voz por EGG SVM, LR, RF, NB Wu et al (2021) [39] Imagens Vídeo SVM Sidhu (2019) [40] Imagens ressônancia magnética Principal Component Analysis (PCA), Independent Component Analysis (ICA), SVM Hasan, Jailani and Tahir (2018) [41] Imagens 3D movimentos KNN, SVM, NN Vijayalakshmi et al (2020) [42] Comportamentais demográficos NB, RF, LR Akter et al (2019) [43] Comportamentais biológicos SVM, DT, LR Roopa and Prasad (2019) [44] Imagens ressônancia magnética SVM, RF, DNN Huang, Liu and Tan (2020) [45] Imagens ressônancia magnética SVM Aslam et al (2021) [46] Imagens [48].…”

Section: Trabalhos Relacionadosunclassified

Autism Spectrum Disorder Diagnosis Assistance using Machine Learning

Artoni

Barbosa

Morandini

2022

RITA

View full text Add to dashboard Cite

Autism Spectrum Disorder (ASD) is a common but complex disorder to diagnose since there are no imaging or blood tests that can detect ASD. Several techniques can be used, such as diagnostic scales that contain specific questionnaires formulated by specialists that serve as a guide in the diagnostic process. In this paper, Machine Learning (ML) was applied on three public databases containing AQ-10 test results for adults, adolescents, and children; as well as other characteristics that could influence the diagnosis of ASD. Experiments were carried out on the databases to list which attributes would be truly relevant for the diagnosis of ASD using ML, which could be of great value for medical students or residents, and for physicians who are not specialists in ASD. The experiments have shown that it is possible to reduce the number of attributes to only 5 while maintaining an Accuracy above 0.9. In the other Database to maintain the same level of Accuracy, the fewer attribute numbers were 7. The Support Vector Machine stood out from the others algorithms used in this paper, obtaining superior results in all scenarios.

show abstract

Section: Trabalhos Relacionadosunclassified

Autism Spectrum Disorder Diagnosis Assistance using Machine Learning

Artoni

Barbosa

Morandini

2022

RITA

View full text Add to dashboard Cite

show abstract

“…Some of the performance indicators used is Brier Score, Precision, Recall and F1-score. (13) F-Score is harmonic mean relation of precision and recall.…”

Section: B Probability Calibration Assessmentmentioning

confidence: 99%

Identification of Best Fit Learning Models Based on Calibration for Better Classification of Autism

S.,

Prasad

2020

IJEAT

View full text Add to dashboard Cite

This paper is intended to exhibit the novel approach to improve the efficiency of the supervised learning models towards the accuracy of the predictions made to classify the autism from that of the normal subject. The state of the art is about 60-75% of Autism classification accuracy. The early prediction of autism plays a vital role as the rise of autism is alarming. The invasive way to analyze the problem at the earliest would render much support to the Autism Spectrum Disorder (ASD) community. In this work, various supervised learning models are first tested on 1101 subjects with 530 ASD subjects and 571 Normal subjects. The Datasets worked are collected from Autism Brain Imaging Data Exchange (ABIDE) repository. The performance measure is calibrated in terms of Brier score which is an accuracy measure of the predictions in probabilistic way. After assessing in probabilistic way, the statistically emphasized models are then evaluated for the same set of data to validate the prediction model efficiency with their statistical measures made and hence developing the confidence of the model selection for better classification based on probability calibration (CAL). The performance evaluation of the model is tested with probability calibrated assessment and found that for given dataset the SVM and Logistic Regression provided better accuracy measure compared to other considered learning models. It is necessary to frame a hypothesis measure on the dataset before any model is deployed. This approach helps to identify the desired and validated supervised model for the given data samples.

show abstract

A Selection of an Optimal Framework Identifying the Prominent Autism Risk Gene Biomarkers from Gene Expression Data Using Neural Network

Roopa

Prasad

2021

SN COMPUT. SCI.

View full text Add to dashboard Cite

Concatenating framework in ASD analysis towards research progress

Cited by 6 publications

References 10 publications

Autism Spectrum Disorder Diagnosis Assistance using Machine Learning

Autism Spectrum Disorder Diagnosis Assistance using Machine Learning

Identification of Best Fit Learning Models Based on Calibration for Better Classification of Autism

A Selection of an Optimal Framework Identifying the Prominent Autism Risk Gene Biomarkers from Gene Expression Data Using Neural Network

Contact Info

Product

Resources

About