Enhancing Breast Cancer Detection: A Machine Learning Approach for Early Diagnosis and Classification
Aditi Sawant, Divya Patil, Dimple Khuman, Yogesh Pingle, Vinayak Shinde
Abstract
Millions of new cases of breast cancer are discovered worldwide each year, which is a serious health risk. For treatment options to be directed and patient outcomes to be improved, a timely and correct diagnosis is essential. Medical diagnostics has shown machine learning, and more specifically logistic regression, to be a useful tool. This work uses a logistic regression model to show how machine learning may be used practically to categorize breast cancer cases. The main goal is to develop a model that can reliably classify breast tissue as benign, malignant, or normal based on medical photos in order to aid in the early diagnosis of cancer. By reducing reliance on arbitrary human judgements, this method aims to improve the consistency and effectiveness of the diagnostic procedure.