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High interpretability and ease of understanding decision trees have made
them one of the most widely used machine learning algorithms. The key to building
efficient and effective decision trees is to use the suitable splitting method. This
paper proposes a new splitting approach to produce a tree based on the T-entropy criterion
for the splitting method. The method presented on three data sets is examined
by 11 evaluation criteria. The results show that the introduced method in making
the decision tree has a more accurate performance than the well-known methods of
Gini index, Shannon, Tisalis, and Renny entropies and can be used as an alternative
method in producing the decision tree.

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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.