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Enhancing Breast Cancer Diagnosis through Predictive Analytics

Author

Listed:
  • Shruti Balla
  • Suyesha Patil
  • Vaishnavi Kashid
  • Sandhya Kokate
  • Ashwini Birajdar
  • S. M. Shinde

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.

Suggested Citation

  • Shruti Balla & Suyesha Patil & Vaishnavi Kashid & Sandhya Kokate & Ashwini Birajdar & S. M. Shinde, 2024. "Enhancing Breast Cancer Diagnosis through Predictive Analytics," International Journal of Scientific Research in Computer Science, Engineering and Information Technology, International Journal of Scientific Research in Computer Science, Engineering and Information Technology, vol. 10(6), pages 879-887, November.
    • Handle: RePEc:jbh:ijsrcs:v10:y2024:i6:id:482
    DOI: 10.32628/CSEIT241061133
    Note: Article URL: https://ijsrcseit.com/home/article/view/CSEIT241061133
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