EARLY DETECTION OF BREAST CANCER USING MACHINE LEARNING

Abstract

Breast cancer is among the most prevalent and life-threatening illnesses in women globally. Early
detection is essential in enhancing survival rates and decreasing mortality. Conventional diagnostic
procedures, including mammography and biopsy, may be time-consuming and may not always
identify cancer in its initial stages. With the developments in artificial intelligence and data
analytics, machine learning algorithms have become effective tools for the analysis of medical
imaging, pattern recognition, and the detection of malignancies with high accuracy. Such
algorithms, such as support vector machines, decision trees, and deep learning networks, have the potential to help radiologists by offering automated, accurate, and fast cancer detection that
improves the decision-making process.
The present study aims at using machine learning methods for the early diagnosis of breast cancer
from examinations of different patient information, such as histopathological images and clinical
parameters. The method proposed here is based on training large numbers of predictive models
over large datasets to identify cancerous tissues at an early stage with low false positives and false
negatives. With the integration of advanced classification methods and feature selection strategies,
the system enhances diagnostic accuracy and helps medical professionals make decisions at the
right time. Introduction of such intelligent systems does not only facilitate early diagnosis but also
helps with personalized treatment planning, ultimately resulting in improved patient outcomes and
a notable decrease in healthcare expenditure.