Performance Comparison of Convolutional Neural Network with Traditional Machine Learning Methods in Adult Autism Detection
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Nurhadi Wijaya
Sri Hasta Muliani
Maisarah Nurain
Abstract
Diagnosing Autism Spectrum Disorder (ASD) in adults is a challenging task, requiring precise and efficient early detection methods. However, there is limited research in this area. Hence, this study seeks to address this gap by evaluating the effectiveness of Convolutional Neural Networks (CNNs) compared to traditional machine learning techniques for detecting autism in adults. The study introduces a CNN-based model and conducts a performance comparison with conventional algorithms such as Support Vector Machine (SVM), Decision Tree, K-Nearest Neighbors (KNN), Gaussian Naive Bayes (GNB), and Gradient Boosting (GBoost). The objectives are to evaluate the efficacy of CNNs in adult autism detection, identify algorithm strengths and weaknesses, and explore healthcare implications. The research utilizes the Autism Screening on Adults dataset, with 704 records and 21 features, employing preprocessing steps to optimize data quality. The proposed CNN model encompasses convolutional layers, max-pooling, dropout, and dense layers, while baseline algorithms serve as benchmarks. Evaluation metrics include the Confusion Matrix and Classification Report. The CNN model achieved remarkable accuracy (99%) and precision in adult autism detection, outperforming traditional algorithms. SVM emerged as the closest competitor but fell short. This study underscores CNN's potential for precise autism detection in adults, with implications for early intervention and telehealth applications. The research highlights CNNs' effectiveness and superiority over traditional machine learning algorithms, suggesting their promise for accurate diagnosis. Future research opportunities include expanding datasets, optimizing model parameters, and addressing ethical considerations for practical healthcare implementation
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Copyright (c) 2024 Nurhadi Wijaya, Sri Hasta Muliani, Maisarah Nurain
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References
H. Dong, D. Chen, L. Zhang, H. Ke, and X. Li, “Subject sensitive EEG discrimination with fast reconstructable CNN driven by reinforcement learning: A case study of ASD evaluation,” Neurocomputing, vol. 449, pp. 136–145, Aug. 2021, doi: 10.1016/j.neucom.2021.04.009.
P. Mohan and I. Paramasivam, “Feature reduction using SVM-RFE technique to detect autism spectrum disorder,” Evol Intell, vol. 14, no. 2, pp. 989–997, Jun. 2021, doi: 10.1007/s12065-020-00498-2.
M. Yang et al., “Large-Scale Brain Functional Network Integration for Discrimination of Autism Using a 3-D Deep Learning Model,” Front Hum Neurosci, vol. 15, Jun. 2021, doi: 10.3389/fnhum.2021.687288.
F. Hassan, S. F. Hussain, and S. M. Qaisar, “Epileptic Seizure Detection Using a Hybrid 1D CNN-Machine Learning Approach from EEG Data,” J Healthc Eng, vol. 2022, pp. 1–16, Nov. 2022, doi: 10.1155/2022/9579422.
X. Kuang, F. Wang, K. M. Hernandez, Z. Zhang, and R. L. Grossman, “Accurate and rapid prediction of tuberculosis drug resistance from genome sequence data using traditional machine learning algorithms and CNN,” Sci Rep, vol. 12, no. 1, p. 2427, Feb. 2022, doi: 10.1038/s41598-022-06449-4.
G. Ahmed, T. Chu, and K. Loo, “Novel Multicolumn Kernel Extreme Learning Machine for Food Detection via Optimal Features from CNN,” arXiv preprint arXiv:2205.07348, 2022.
L. Shao, C. Fu, Y. You, and D. Fu, “Classification of ASD based on fMRI data with deep learning,” Cogn Neurodyn, vol. 15, no. 6, pp. 961–974, Dec. 2021, doi: 10.1007/s11571-021-09683-0.
H. Sewani and R. Kashef, “An Autoencoder-Based Deep Learning Classifier for Efficient Diagnosis of Autism,” Children, vol. 7, no. 10, p. 182, Oct. 2020, doi: 10.3390/children7100182.
K. Vakadkar, D. Purkayastha, and D. Krishnan, “Detection of Autism Spectrum Disorder in Children Using Machine Learning Techniques,” SN Comput Sci, vol. 2, no. 5, p. 386, Sep. 2021, doi: 10.1007/s42979-021-00776-5.
S. R, A. SL, J. M. Chatterjee, A. Alaboudi, and N. Jhanjhi, “A Machine Learning Way to Classify Autism Spectrum Disorder,” International Journal of Emerging Technologies in Learning (iJET), vol. 16, no. 06, p. 182, Mar. 2021, doi: 10.3991/ijet.v16i06.19559.
Y. Tang and H. Li, “Comparing the performance of machine learning methods in predicting soil seed bank persistence,” Ecol Inform, vol. 77, p. 102188, Nov. 2023, doi: 10.1016/j.ecoinf.2023.102188.
Z. Wu and H. He, “Traditional Machine Learning Models for Building Energy Performance Prediction: A Comparative Research,” Machine Learning Research, May 2023, doi: 10.11648/j.mlr.20230801.11.
D. Liang, X. Jin, Y. Yuan, and R. Zou, “Performance Analysis of Machine Learning Methods,” J Phys Conf Ser, vol. 2428, no. 1, p. 012039, Feb. 2023, doi: 10.1088/1742-6596/2428/1/012039.
M. S. Farooq, R. Tehseen, M. Sabir, and Z. Atal, “Detection of autism spectrum disorder (ASD) in children and adults using machine learning,” Sci Rep, vol. 13, no. 1, p. 9605, Jun. 2023, doi: 10.1038/s41598-023-35910-1.
N. Al-Qazzaz, S. Jaffer, I. Abdulazez, and T. Yousif, “Data Mining for Autism Spectrum Disorder detection among Adults,” Al-Nahrain Journal for Engineering Sciences, vol. 25, no. 4, pp. 142–151, Dec. 2022, doi: 10.29194/NJES.25040142.
S. Zahan, Z. Gilani, G. M. Hassan, and A. Mian, “Human gesture and gait analysis for autism detection,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2023, pp. 3328–3337.
Y. Fan, H. Xiong, and G. Sun, “DeepASDPred: a CNN-LSTM-based deep learning method for Autism spectrum disorders risk RNA identification,” BMC Bioinformatics, vol. 24, no. 1, p. 261, Jun. 2023, doi: 10.1186/s12859-023-05378-x.
A. A. Artoni, C. Barbosa, and M. Morandini, “Autism Spectrum Disorder Diagnosis Assistance using Machine Learning,” Revista de Informática Teórica e Aplicada, vol. 29, no. 3, pp. 36–53, Dec. 2022, doi: 10.22456/2175-2745.126309.
S. Hazim Hammed and A. S. Albahri, “Unlocking the Potential of Autism Detection: Integrating Traditional Feature Selection and Machine Learning Techniques,” Applied Data Science and Analysis, pp. 42–58, May 2023, doi: 10.58496/ADSA/2023/003.
A. V. Shinde and D. D. Patil, “A Multi-Classifier-Based Recommender System for Early Autism Spectrum Disorder Detection using Machine Learning,” Healthcare Analytics, vol. 4, p. 100211, Dec. 2023, doi: 10.1016/j.health.2023.100211.
A. El Mouatasim and M. Ikermane, “Control learning rate for autism facial detection via deep transfer learning,” Signal Image Video Process, vol. 17, no. 7, pp. 3713–3720, Oct. 2023, doi: 10.1007/s11760-023-02598-9.
M. Diqi and S. H. Mulyani, “Implementation of CNN for Plant Leaf Classification,” International Journal of Informatics and Computation, vol. 2, no. 2, p. 1, Mar. 2021, doi: 10.35842/ijicom.v2i2.28.
I. M. I’tisyam, N. Wijaya, and R. Pradila, “Implementation of Deep Learning for Classification of Mushroom Using CNN Algorithm,” International Journal of Informatics and Computation, vol. 5, no. 1, pp. 1–9, 2023.
A. D. Ronaldo, “Effective Soil Type Classification Using Convolutional Neural Network,” International Journal of Informatics and Computation, vol. 3, no. 1, p. 20, Oct. 2021, doi: 10.35842/ijicom.v3i1.33.
C. Zhang, D. Jia, L. Wang, W. Wang, F. Liu, and A. Yang, “Comparative research on network intrusion detection methods based on machine learning,” Comput Secur, vol. 121, p. 102861, Oct. 2022, doi: 10.1016/j.cose.2022.102861.
C. Rogerson and M. Hall, “Methodological progress note: Machine learning methods in healthcare research,” J Hosp Med, vol. 18, no. 5, pp. 431–434, May 2023, doi: 10.1002/jhm.13078.
H. Huang et al., “Research on prediction methods of formation pore pressure based on machine learning,” Energy Sci Eng, vol. 10, no. 6, pp. 1886–1901, Jun. 2022, doi: 10.1002/ese3.1112.
M. Pushpa and M. Sornamageswari, “Early stage autism detection using ANFIS and extreme learning machine algorithm,” Journal of Intelligent & Fuzzy Systems, vol. 45, no. 3, pp. 4371–4382, Aug. 2023, doi: 10.3233/JIFS-231608.
B. Banire, D. Al Thani, and M. Qaraqe, “One size does not fit all: detecting attention in children with autism using machine learning,” User Model User-adapt Interact, vol. 34, no. 2, pp. 259–291, Apr. 2024, doi: 10.1007/s11257-023-09371-0.
A. L. C. Alves, J. J. de Paula, D. M. de Miranda, and M. A. Romano-Silva, “The Autism Spectrum Quotient in a sample of Brazilian adults: analyses of normative data and performance,” Dement Neuropsychol, vol. 16, no. 2, pp. 244–248, Jun. 2022, doi: 10.1590/1980-5764-dn-2021-0081.
S. C. Taylor et al., “Contrasting Views of Autism Spectrum Traits in Adults, Especially in Self-Reports vs. Informant-Reports for Women High in Autism Spectrum Traits,” J Autism Dev Disord, vol. 54, no. 3, pp. 1088–1100, Mar. 2024, doi: 10.1007/s10803-022-05822-6.
M. D. Hossain, M. A. Kabir, A. Anwar, and M. Z. Islam, “Detecting autism spectrum disorder using machine learning techniques,” Health Inf Sci Syst, vol. 9, no. 1, p. 17, Dec. 2021, doi: 10.1007/s13755-021-00145-9.
E. Sahin, S. Bury, R. Flower, L. Lawson, A. Richdale, and D. Hedley, “Psychometric Evaluation of an Australian Version of the Vocational Index for Adults with Autism,” Autism in Adulthood, vol. 2, no. 3, pp. 185–192, Sep. 2020, doi: 10.1089/aut.2019.0060.
V. Yaneva, L. A. Ha, S. Eraslan, Y. Yesilada, and R. Mitkov, “Detecting High-Functioning Autism in Adults Using Eye Tracking and Machine Learning,” IEEE Transactions on Neural Systems and Rehabilitation Engineering, vol. 28, no. 6, pp. 1254–1261, Jun. 2020, doi: 10.1109/TNSRE.2020.2991675.
L. B. Ribeiro, U. K. M. A. Da Silva, A. Zaferiou, and M. Da Silva Filho, “COMPARISON OF MACHINE LEARNING MODELS FOR AUTOMATED AUTISM DIAGNOSIS,” REVISTA FOCO, vol. 16, no. 6, p. e2311, Jun. 2023, doi: 10.54751/revistafoco.v16n6-104.
K. O’Hearn and A. Lynn, “Age differences and brain maturation provide insight into heterogeneous results in autism spectrum disorder,” Front Hum Neurosci, vol. 16, Feb. 2023, doi: 10.3389/fnhum.2022.957375.
S. A. Justus, S. Mirjalili, P. S. Powell, and A. Duarte, “Neural reinstatement of context memory in adults with autism spectrum disorder,” Cerebral Cortex, vol. 33, no. 13, pp. 8546–8556, Jun. 2023, doi: 10.1093/cercor/bhad139.