Machine Learning Based Ensemble Classifier using Wisconsin Dataset For Breast Cancer Prediction

Abstract

Subclass of Artificial Intelligence, machine learning integrates several optimizations, probabilistic, and statistical methodologies to help computers learn from prior occurrences and find it challenging to spot patterns in intricate datasets, noised and large datasets. As a result, machine learning in cancer diagnosis and detection has increased. In women, breast cancer is the utmost common malignancy. The study aims to predict breast cancer by comparing widely used machine learning algorithms and methodologies, neural networks, Random Forest, and Boosting, upon Wisconsin Diagnosis Breast Cancer data set to inspect the performance of critical characteristics such as precision and accuracy. This study's accurate, competitive results can be used for detection and treatment.