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Journals / Journal of Soft Computing and Artificial Intelligence / 2023 / Cilt: 4 Sayı: 1

Comparative Analysis of Globalisation Techniques for Medical Document Classification

Bekir PARLAK
Salih Berkan AYDEMİR
Pages
7–14
Publication Date
2023-06-25
DOI
—
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AI Summary & Key Takeaways

AI Insight
⚡ TL;DR(Too Long; Didn't Read)

Classifiers are MNB, DT, and SVM.

📌 3 Key Takeaways

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Objective & Hypothesis:Medical document classification is one of the important topics of text mining.
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Key Findings & Data:To observe how successful local feature selection methods in the field of text classification affect the classification performance on these two equivalent data sets by applying different globalisation techniques.
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Conclusion & Impact:Classifiers are MNB, DT, and SVM.
🔬Design:Research Article
📍Scope:Comprehensive Sample
⚙️Variables:Medical documents · Text Classification · Feature selection

Abstract

Medical document classification is one of the important topics of text mining. Globalisation techniques play a major role in text classification. It is also known that globalisation techniques play an important role in text classification. Our aim in the study is to conduct a detailed analysis on two data sets with English and Turkish content by using medical text summaries of Turkish articles. These datasets consist of Turkish and English text summaries of the same articles. To observe how successful local feature selection methods in the field of text classification affect the classification performance on these two equivalent data sets by applying different globalisation techniques. The feature selection methods used are CHI2, MI, OR, WLLR. Globalisation techniques are SUM, AVG, MAX. Classifiers are MNB, DT, and SVM.

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Cite This Article

PARLAK, B., AYDEMİR, S. B. (2023). Comparative Analysis of Globalisation Techniques for Medical Document Classification. Journal of Soft Computing and Artificial Intelligence, Cilt: 4 Sayı: 1, 7–14. https://ajindex.com/articles/952507

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References (1)

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