Year: 2024
Type: article
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.
Authors: Shruti Balla, Suyesha Patil, Vaishnavi Kashid, Sandhya Kokate, Ashwini Birajdar, S. M. Shinde
Language: English
Topic: AI in cancer detection
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: diamond
OpenAlex ID: W4404760128