scholarly journals Prediksi Gejala Autism Spectrum Disorders pada Remaja Menggunakan Optimasi Particle Swarm Optimization dan Algoritma Support Vector Machine

2020 ◽  
Vol 4 (2) ◽  
pp. 113
Author(s):  
Ilham Kurniawan
Keyword(s):  
Autism Spectrum Disorder ◽  
Support Vector Machine ◽  
Feature Selection ◽  
Particle Swarm ◽  
Autism Spectrum ◽  
Classification Algorithm ◽  
Spectrum Disorder ◽  
Support Vector ◽  
Svm Classification ◽  
Proposed Model

Abstrak: Telah ada peningkatan prevalensi diagnosis Autism Spectrum Disorder (ASD) secara global selama dekade terakhir. Perkiraan prevalensi ASD yang diperbarui dan keseluruhan di Asia akan membantu para profesional kesehatan untuk mengembangkan strategi kesehatan masyarakat yang relevan. Dalam penelitian ini, mengusulkan metode untuk prediksi gejala ASD menggunakan teknik integrasi seleksi fitur PSO dan algoritma Support Vector Machine. Penelitian ini menggunakan dataset dari UCI repository. Model yang diusulkan meliputi penerapan seleksi fitur menggunakan  particle swarm optimization (PSO), dengan algoritma pengklasifikasi. Hasil akhir akan dilakukan perbandingan pengujian dan analisa terhadap model prediksi yang memiliki tingkat akurasi tertinggi atau terbaik dalam prediksi gejala ASD. Dalam penelitian ini menggunakan dataset UCI repository yaitu data ASD pada remaja, data tersebut memiliki jumlah data sebanyak 104 instance dan 21 atribut, 41 orang tidak menderita ASD dan 63 orang menderita ASD, tools yang digunakan untuk menerapkan model usulan menggunakan aplikasi Weka versi 3.8.4. Untuk mengetahui model usulan yang diajukan pada penelitian ini, pertama menguji dengan klasifikasi tunggal SVM, dan kedua, menguji dengan seleksi fitur PSO dan algoritma klasifikasi SVM. Untuk mengetahui apakah seleksi fitur PSO berpengaruh terhadap performa algoritma klasifikasi SVM. Pengujian pertama, nilai akurasi yang dihasilkan oleh algoritma klasifikasi SVM adalah sebesar 89.42%, dan nilai AUC sebesar 0.891. Berdasarkan pengujian yang kedua yaitu menggunakan seleksi fitur PSO, seleksi fitur PSO dapat meningkatkan performa algoritma klasifikasi SVM sebesar 2,88% dan nilai AUC sebesar 0,024.   Kata kunci: Autism Spectrum Disorder, Particle Swarm Optimizatio, Support Vector Machine   Abstract: There has been an increase in the prevalence of diagnoses of Autism Spectrum Disorder (ASD) globally over the past decade. Updated and overall ASD prevalence estimates in Asia will help health professionals to develop relevant public health strategies. In this study, proposing a method for ASD symptom prediction using PSO feature selection integration techniques and the Support Vector Machine algorithm. This study uses a dataset from the UCI repository. The proposed model includes the application of feature selection using particle swarm optimization (PSO), with the classification algorithm. The final result will be a comparison test and analysis of prediction models that have the highest or best accuracy in predicting ASD symptoms. In this study using the UCI repository dataset, ASD data on adolescents, the data has 104 data and 21 attributes, 41 people do not suffer from ASD and 63 people suffer from ASD, tools used to implement the proposed model using the Weka application version 3.8.4 . To find out the proposed model proposed in this study, firstly testing with SVM single classification, and secondly, testing with PSO feature selection and SVM classification algorithm. To find out whether the PSO feature selection affects the performance of the SVM classification algorithm. The first test, the accuracy value generated by the SVM classification algorithm is 89.42%, and the AUC value is 0.891. Based on the second test using PSO feature selection, PSO feature selection can improve the performance of the SVM classification algorithm by 2.88% and the AUC value of 0.024.   Keywords: Autism Spectrum Disorder, Particle Swarm Optimizatio, Support Vector Machine.

scholarly journals Prediction of autism spectrum disorder diagnosis using nonlinear measures of language-related EEG at 6 and 12 months

2021 ◽  
Vol 13 (1) ◽  
Author(s):  
Fleming C. Peck ◽  
Laurel J. Gabard-Durnam ◽  
Carol L. Wilkinson ◽  
William Bosl ◽  
Helen Tager-Flusberg ◽  
...  
Keyword(s):  
Autism Spectrum Disorder ◽  
Feature Selection ◽  
Language Learning ◽  
Predictive Accuracy ◽  
Pearson Correlation ◽  
Autism Spectrum ◽  
Spectrum Disorder ◽  
Support Vector ◽  
Phoneme Discrimination ◽  
Co Morbidity

Abstract Background Early identification of autism spectrum disorder (ASD) provides an opportunity for early intervention and improved developmental outcomes. The use of electroencephalography (EEG) in infancy has shown promise in predicting later ASD diagnoses and in identifying neural mechanisms underlying the disorder. Given the high co-morbidity with language impairment, we and others have speculated that infants who are later diagnosed with ASD have altered language learning, including phoneme discrimination. Phoneme learning occurs rapidly in infancy, so altered neural substrates during the first year of life may serve as early, accurate indicators of later autism diagnosis. Methods Using EEG data collected at two different ages during a passive phoneme task in infants with high familial risk for ASD, we compared the predictive accuracy of a combination of feature selection and machine learning models at 6 months (during native phoneme learning) and 12 months (after native phoneme learning), and we identified a single model with strong predictive accuracy (100%) for both ages. Samples at both ages were matched in size and diagnoses (n = 14 with later ASD; n = 40 without ASD). Features included a combination of power and nonlinear measures across the 10‑20 montage electrodes and 6 frequency bands. Predictive features at each age were compared both by feature characteristics and EEG scalp location. Additional prediction analyses were performed on all EEGs collected at 12 months; this larger sample included 67 HR infants (27 HR-ASD, 40 HR-noASD). Results Using a combination of Pearson correlation feature selection and support vector machine classifier, 100% predictive diagnostic accuracy was observed at both 6 and 12 months. Predictive features differed between the models trained on 6- versus 12-month data. At 6 months, predictive features were biased to measures from central electrodes, power measures, and frequencies in the alpha range. At 12 months, predictive features were more distributed between power and nonlinear measures, and biased toward frequencies in the beta range. However, diagnosis prediction accuracy substantially decreased in the larger, more behaviorally heterogeneous 12-month sample. Conclusions These results demonstrate that speech processing EEG measures can facilitate earlier identification of ASD but emphasize the need for age-specific predictive models with large sample sizes to develop clinically relevant classification algorithms.

Modified Cuckoo Search-Support Vector Machine (MCS-SVM) Gene Selection and Classification for Autism Spectrum Disorder (ASD) Gene Expression

2020 ◽  
Vol 18 (11) ◽  
pp. 01-13
Author(s):  
Dr.M. Kalaiarasu ◽  
Dr.J. Anitha
Keyword(s):  
Gene Expression ◽  
Autism Spectrum Disorder ◽  
Support Vector Machine ◽  
Gene Selection ◽  
Cuckoo Search ◽  
Autism Spectrum ◽  
Spectrum Disorder ◽  
Support Vector ◽  
Svm Classifier ◽  
Classification Methods

Autism Spectrum Disorder (ASD) is a neuro developmental disorder characterized by weakened social skills, impaired verbal and non-verbal interaction, and repeated behavior. ASD has increased in the past few years and the root cause of the symptom cannot yet be determined. In ASD with gene expression is analyzed by classification methods. For the selection of genes in ASD, statistical philtres and a wrapper-based Geometric Binary Particle Swarm Optimization-Support Vector Machine (GBPSO-SVM) algorithm have recently been implemented. However GBPSO has provides lesser accuracy, if the dataset samples are large and it cannot directly apply to multiple output systems. To overcome this issue, Modified Cuckoo Search-Support Vector Machine (MCS-SVM) based wrapper feature selection algorithm is proposed which improves the accuracy of the classifier in ASD. This work consists of three major steps, (i) preprocessing, (ii) gene selection, and (iii) classification. Firstly, preprocessing is performed by mean or median ratios close to unity was removed from original gene dataset; based on this samples are reduced from 54,613 to 9454. Secondly, gene selection is performed by using statistical filters and wrapper algorithm. Statistical filters methods like Wilcox on Rank Sum test (WRS), Class Correlation (COR) function and Two-sample T-test (TT) were applied in parallel to a ten-fold cross validation range of the most discriminatory genes. In the wrapper algorithm, Modified Cuckoo Search (MCS) is also proposed to gene selection. This step decreases the number of genes of the dataset by removing genes. Finally, SVM classifier combined forms of gene subsets for grading. The autism microarray dataset used in the analysis was downloaded from the benchmark public repository Gene Expression Omnibus (GEO) (National Center for Biotechnology Information (NCBI)). The classification methods are measured in terms of the metrics like precision, recall, f-measure and accuracy. Proposed MCS-SVM classifier achieves highest accuracy when compared Linear Regression (LR), and GBPSO-SVM classifiers.

scholarly journals The Genetic-Evolutionary Random Support Vector Machine Cluster Analysis in Autism Spectrum Disorder

IEEE Access ◽  
2019 ◽  
Vol 7 ◽  
pp. 30527-30535 ◽  
Author(s):  
Xia-an Bi ◽  
Yingchao Liu ◽  
Qi Sun ◽  
Xianhao Luo ◽  
Haiyan Tan ◽  
...  
Keyword(s):  
Autism Spectrum Disorder ◽  
Cluster Analysis ◽  
Support Vector Machine ◽  
Autism Spectrum ◽  
Spectrum Disorder ◽  
Support Vector

scholarly journals Prediction of autism spectrum disorder diagnosis using nonlinear measures of language-related EEG at 6 and 12 months

2021 ◽  
Author(s):  
Fleming C. Peck ◽  
Laurel J. Gabard-Durnam ◽  
Carol L Wilkinson ◽  
William Bosl ◽  
Helen Tager-Flusberg ◽  
...  
Keyword(s):  
Autism Spectrum Disorder ◽  
Feature Selection ◽  
Language Learning ◽  
Predictive Accuracy ◽  
Pearson Correlation ◽  
Autism Spectrum ◽  
Spectrum Disorder ◽  
Support Vector ◽  
Phoneme Discrimination ◽  
Co Morbidity

Abstract Background: Early identification of autism spectrum disorder (ASD) provides opportunity for early intervention and improved outcomes. Electroencephalography (EEG) use in infants has shown promise in predicting later ASD diagnoses and in identifying neural mechanisms underlying the disorder. Given the high co-morbidity with language impairment, we and others have speculated that infants who are later diagnosed with ASD have altered language learning, including phoneme discrimination. Phoneme learning occurs rapidly in infancy, so altered neural substrates either during or after the first year may serve as early, accurate indicators of later autism diagnosis. Methods: Using longitudinal EEG data collected during a passive phoneme task in infants with high familial risk for ASD, we compared the predictive accuracy of a combination of feature selection and machine learning models at 6 months (during phoneme learning) versus 12 months (after phoneme learning), and identified a single model with strong predictive accuracy (100%) for both ages. Samples at both ages were matched in size and diagnoses (n=14 with later ASD; n= 40 without ASD). Features included a combination of power and nonlinear measures across 10-20 electrodes and 6 frequency bands. Predictive features at each age were compared both by feature characteristics and EEG scalp location. Results: Using a combination Pearson correlation feature selection and support vector machine classifier 100% predictive diagnostic accuracy was observed at both 6 and 12 months. Predictive features differed between the models trained on 6- versus 12- month data. At 6-months, predictive features were biased to measures from central electrodes, power measures, and measures in the alpha range. At 12-months, predictive features were more distributed between power and nonlinear measures, and biased toward measures in the beta range. Conclusions: These results demonstrate that speech processing EEG measures can facilitate earlier identification of ASD but emphasize the need for age-specific predictive models with large sample sizes to develop clinically relevant classification algorithms.

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