Early stage analysis of breast cancer using intelligent system

Arpita Nath Boruah and Mrinal Goswami

International Journal of Data Mining, Modelling and Management, 2024, vol. 16, issue 4, 443-454

Abstract: Breast cancer (BC) poses a considerable global health concern for women, which makes a significant issue for women's well-being worldwide. It is crucial to develop a system that can proactively identify the critical risk factors associated with BC. The present study introduces an intelligent system for BC by analysing risk factors (IS-BC-analysing-RF) which utilises decision tree rules to identify the primary risk factors underlying BC accurately. The rules are processed based on the proposed score function to get the most relevant ones. Finally, using the sequential search approach, the critical risk factors are identified along with their respective ranges. Based on the simulation results using University of California at Irvine (UCI) repository BC dataset, the findings indicate that the proposed IS-BC-analysing-RF system is highly significant and has the potential to effectively mitigate the risk of BC by targeting and managing one or two crucial risk factors.

Keywords: decision system; breast cancer; decision tree; machine learning; risk factor. (search for similar items in EconPapers)
Date: 2024
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Persistent link: https://EconPapers.repec.org/RePEc:ids:ijdmmm:v:16:y:2024:i:4:p:443-454

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