Year: 2025
Type: article
Abstract: Breast cancer remains one of the most common and life-threatening diseases among women worldwide, where early and accurate diagnosis plays a vital role in patient survival.Traditional diagnostic methods, though effective, are often time-consuming and prone to human error.Machine Learning (ML) offers a promising solution by automatically learning patterns from medical data and providing reliable predictions.In the proposed work, we implement a breast cancer prediction system using the Random Forest algorithm, which is well-suited for medical classification tasks due to its robustness, ability to handle high-dimensional data, and reduced risk of overfitting.Additionally, we extend the model into an interactive system that allows real-time prediction from user-provided patient data.The contribution of this work lies in demonstrating Random Forest's effectiveness for breast cancer diagnosis, validating its performance against existing approaches, and showcasing its potential for integration into practical healthcare decision-support tools
Language: English
Subfield: Health Information Management
Field: Health Professions
Domain: Health Sciences
Open Access status: hybrid
OpenAlex ID: W4414543371
FWCI:
Cites:
Cited by:
Related to: