Enhancing Breast Cancer Diagnosis through Predictive Analytics

Shruti Balla, Suyesha Patil, Vaishnavi Kashid, Sandhya Kokate, Ashwini Birajdar and S. M. Shinde

International Journal of Scientific Research in Computer Science, Engineering and Information Technology, 2024, vol. 10, issue 6, 879-887

Abstract: Breast cancer is one of the leading causes of cancer-related deaths among women globally. Early and accurate diagnosis is crucial for improving patient outcomes. Predictive analytics, leveraging machine learning and artificial intelligence (AI), offers promising advancements in breast cancer diagnosis by enhancing diagnostic accuracy, reducing false positives, and enabling personalized treatment plans. This research explores the role of predictive analytics in breast cancer diagnosis, covering methodologies, benefits, challenges, and future directions. Creating a power BI dashboard for visualization. By analyzing data from various sources, including mammograms, biopsies, and genetic profiles, predictive models can significantly improve diagnostic precision, thus contributing to better healthcare delivery.

Keywords: breast cancer; artificial intelligence; machine learning; power BI (search for similar items in EconPapers)
Date: 2024
Note: Article URL: https://ijsrcseit.com/home/article/view/CSEIT241061133
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Persistent link: https://EconPapers.repec.org/RePEc:jbh:ijsrcs:v10:y2024:i6:id:482

DOI: 10.32628/CSEIT241061133

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