Breast Cancer Prediction Using Machine Learning on Parallel Computing

Abstract

Breast cancer remains one of the most common and significant health challenges for women worldwide. Early detection is crucial for improving survival rates, yet traditional methods often fail to offer timely and accurate predictions. Recent advancements in artificial intelligence (AI) and machine learning (ML) have provided promising tools for enhancing diagnostic processes. This paper presents a practical application of machine learning techniques to predict breast cancer, leveraging parallel computing for improved processing efficiency. By utilizing a dataset of diagnostic features, the study demonstrates how ML algorithms, implemented through a parallel computing framework, can offer accurate predictions of breast cancer incidence. The proposed system utilizes Python and the Ray framework to implement a distributed approach for model training and evaluation, showing substantial potential for scalable, real-time prediction systems in healthcare.