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:: Volume 18, Issue 2 (2-2025) ::
JSS 2025, 18(2): 0-0 Back to browse issues page
Early Detection of Down Syndrome Using Machine Learning Algorithms
Abdolreza Sayyareh * , Saeide Abdollahzadeh
Abstract:   (1326 Views)
Non-invasive NIPT test has been used in trisomy 21 screening. However, there is a possibility of misdiagnosis in the methods used to diagnose Down syndrome. Therefore, it is essential to provide a process that can be used alongside these methods to improve efficiency. The main goal of this article is to design a model based on machine learning algorithms for the early diagnosis of Down syndrome. Machine learning algorithms such as support vector machine, simple Bayes, decision tree, random forest, and nearest neighbor, which are frequently used to improve the diagnosis of disorders, have been implemented on the mentioned dataset. The performance of each model on the Down syndrome dataset was investigated, and the most suitable model for this purpose was introduced.
Keywords: Decision tree algorithm, Down syndrome, Machine learning, Support vector machine algorithm
Full-Text [PDF 872 kb]   (684 Downloads)    
Type of Study: Applied | Subject: Biostatistics
Received: 2024/03/14 | Accepted: 2024/05/30 | Published: 2024/12/2
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Sayyareh A, Abdollahzadeh S. Early Detection of Down Syndrome Using Machine Learning Algorithms. JSS 2025; 18 (2)
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Creative Commons License This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
Volume 18, Issue 2 (2-2025) Back to browse issues page
مجله علوم آماری – نشریه علمی پژوهشی انجمن آمار ایران Journal of Statistical Sciences

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