Machine Learning-based Breast Cancer Classification Using Logistic Regression and Random Forest

Read the full article See related articles

Discuss this preprint

Start a discussion What are Sciety discussions?

Listed in

This article is not in any list yet, why not save it to one of your lists.
Log in to save this article

Abstract

Breast cancer categorization is vital for prompt identification and strategic therapy planning. In this research, a machine learning model was created utilizing the Breast Cancer Wisconsin (Diagnostic) dataset to categorize tumors as malignant or benign. The model achieved an overall accuracy of 97.36%, demonstrating strong predictive performance.For the malignant class, the model attained a precision of 0.976, recall of 0.953, and F1-score of 0.965, indicating a strong accuracy in detecting cancerous instances. For the benign class , the model achieved a precision of 0.972, recall of 0.986, and F1-score of 0.979, confirming its effectiveness in correctly classifying non-cancerous cases. The macro average F1-score was 0.972, and the weighted average F1-score was 0.974, further emphasizing equitable performance in both categories.These findings indicate that the suggested classification method offers a reliable and precise diagnostic instrument, with possible uses in clinical decision support systems for breast cancer detection.

Article activity feed