Penerapan Principal Component Analysis pada Model Deteksi Dini Anak Autisme
DOI:
https://doi.org/10.32493/jtsi.v7i2.38935Keywords:
Children, autism, Detection, Principal Component AnalysisAbstract
ASD (Autism Spectrum Disorder) is a neurological disorder that causes lifelong disturbances in children resulting in mental illness. Treatment can help but cannot be cured. Currently ASD is detected by understanding a child's behavior and intellectual activity. This diagnosis can be subjective, time-consuming, inconclusive, does not provide precise insight into genetics and is unsuitable for early detection. In Autism, a major challenge faced in many healthcare conditions is timing of diagnosis. It can take up to 6 months to diagnose a child with autism with certainty because of the lengthy process, and a child must see many different specialists to diagnose autism, from a developmental pediatrician, neurologist, psychiatrist or psychologist. Machine Learning Methods can make relevant changes to speed up the process. In this study, it is proposed to apply PCA (Principal Component Analysis). PCA is basically the basis of multivariate data analysis that applies the projection method. This analysis technique is usually used to summarize multivariate data tables on a large scale so that they can be used as a collection of smaller variables or a summary index. From there, variables are then analyzed to find out certain trends, variable clusters, and outliers. In this study it is proposed to implement the PCA (Principal Component Analysis) algorithm, namely PCA (Principal Component Analysis), Kernel PCA, Sparse PCA, and Incremental PCA. In this study using the experimental method by making applications to implement the proposed algorithm. Then test the model using the secondary dataset and measure the performance of the model. The research results show that the model that applies Sparse PCA gives the best results, which means that the application of PCA can be used to reduce the number of features and increase model performance.
References
Bone, D., Goodwin, M. S., & Lee, M. P. B. C. (2014). Applying Machine Learning to Facilitate Autism Diagnostics : Pitfalls and Promises. 101. https://doi.org/10.1007/s10803-014-2268-6
Constantino, J. N., Lavesser, P. D., Zhang, Y., Abbacchi, A. M., Gray, T., & Todd, R. D. (2007). Rapid Quantitative Assessment of Autistic Social Impairment by Classroom Teachers. Journal of the American Academy of Child and Adolescent Psychiatry, 46(12), 1668–1676. https://doi.org/10.1097/chi.0b013e318157cb23
Dawson, C. W. (2009). Projects in Computing and Information Systems. In Information Systems Journal (Vol. 2). http://www.sentimentaltoday.net/National_Academy_Press/0321263553.Addison.Wesley.Publishing.Company.Projects.in.Computing.and.Information.Systems.A.Students.Guide.Jun.2005.pdf
Koehler, J. C., Dong, M. Sen, Bierlich, A. M., Fischer, S., Späth, J., Plank, I. S., Koutsouleris, N., & Falter-Wagner, C. M. (2024). Machine learning classification of autism spectrum disorder based on reciprocity in naturalistic social interactions. Translational Psychiatry, 14(1), 76. https://doi.org/10.1038/s41398-024-02802-5
Kosmicki, J. A., Sochat, V., Duda, M., & Wall, D. P. (2015). Searching for a Minimal Set of Behaviors for Autism Detection Through Feature Selection-based Machine Learning. Translational Psychiatry, 5(2), e514-7. https://doi.org/10.1038/tp.2015.7
Li, B., Sharma, A., Meng, J., Purushwalkam, S., & Gowen, E. (2017). Applying Machine Learning to Identify Autistic Adults Using Imitation: An Exploratory Study. PLoS ONE, 12(8), 1–19. https://doi.org/10.1371/journal.pone.0182652
Mythili, M. S., & Shanavas, A. R. M. (2014). A Study on Autism Spectrum Disorders using Classification Techniques. International Journal of Soft Computing and Engineering (IJSCE), 5, 2231–2307. http://www.ijsce.org/wp-content/uploads/papers/v4i5/E2433114514.pdf
Qureshi, M. S., Qureshi, M. B., Asghar, J., Alam, F., & Aljarbouh, A. (2023). Prediction and Analysis of Autism Spectrum Disorder Using Machine Learning Techniques. Journal of Healthcare Engineering, 2023, 1–10. https://doi.org/10.1155/2023/9815989
R, V., & R, S. (2018). A Machine Learning based Approach to Classify Autism with Optimum Behavior Sets. International Journal of Engineering & Technology, 7(4), 4216–4219.
Thabtah, F. (2017). Autism Spectrum Disorder Screening: Machine Learning Adaptation and DSM-5 Fulfillment. ACM International Conference Proceeding Series, Part F1293, 1–6. https://doi.org/10.1145/3107514.3107515
Thabtah, F. (2019). Machine Learning in Autistic Spectrum Disorder Behavioral Research: A Review and Ways Forward. Informatics for Health and Social Care, 44(3), 278–297. https://doi.org/10.1080/17538157.2017.1399132
Thabtah, F., Kamalov, F., & Rajab, K. (2018). A New Computational Intelligence Approach to Detect Autistic Features for Autism Screening. International Journal of Medical Informatics, 117, 112–124. https://doi.org/10.1016/j.ijmedinf.2018.06.009
Wall, D. P., Dally, R., Luyster, R., Jung, J. Y., & DeLuca, T. F. (2012). Use of Artificial Intelligence to Shorten the Behavioral Diagnosis of Autism. PLoS ONE, 7(8). https://doi.org/10.1371/journal.pone.0043855
Wall, D. P., Kosmicki, J., Deluca, T. F., Harstad, E., & Fusaro, V. A. (2012). Use of Machine Learning to Shorten Observation-based Screening and Diagnosis of Autism. Translational Psychiatry, 2(December 2011). https://doi.org/10.1038/tp.2012.10
Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2024 Aries Saifudin, Endar Nirmala, Irpan Kusyadi
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
Authors who publish with this journal agree to the following terms:
- Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under a Creative Commons Attribution License that allows others to share the work with an acknowledgement of the work's authorship and initial publication in this journal.
- Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgement of its initial publication in this journal.
- Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work (See The Effect of Open Access).
Jurnal Teknologi Sistem Informasi dan Aplikasi have CC BY-NC or an equivalent license as the optimal license for the publication, distribution, use, and reuse of scholarly work.
In developing strategy and setting priorities, Jurnal Teknologi Sistem Informasi dan Aplikasi recognize that free access is better than priced access, libre access is better than free access, and libre under CC BY-NC or the equivalent is better than libre under more restrictive open licenses. We should achieve what we can when we can. We should not delay achieving free in order to achieve libre, and we should not stop with free when we can achieve libre.
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International (CC BY-NC 4.0) License
YOU ARE FREE TO:
- Share - copy and redistribute the material in any medium or format
- Adapt - remix, transform, and build upon the material for any purpose, even commercially.
- The licensor cannot revoke these freedoms as long as you follow the license terms
_2020_-_7(2)_2024_-_Thumbnail.png)
