Hybrid Feature Selection and Bayesian Optimization with Machine Learning for Breast Cancer Prediction

Breast Cancer is one of the most ubiquitous type of cancer among women and rarely found in men. According to the World Health Organization (WHO), Cancer is defined as an uncontrollable abnormal growth of cells in any organ or tissue of the body. Neoplasm or Malignant tumor are common words that describe cancer. According to a survey conducted by the World Health Organization (WHO), cancer accounts for deaths of approximately 9.6 million people globally, and is the second most prevalent cause of death in humans. Cancer is responsible for one out of every six deaths across the world. The good thing is cancer if diagnosed at an early stage, is likely to be treated successfully resulting in high survival rate and low treatment cost. However, it is quite difficult to diagnose it at an early-stage. Therefore, there is a necessity for an efficient cancer prediction model that can predict breast cancer at an early stage. The proposed model highlights the finest set of features required for the detection of breast cancer. It uses Bayesian optimization technique along with hyper parameter tuning and feature selection techniques to decrease the number of parameters by almost 40% while maintaining high accuracy. The best accuracy of 96.2% is obtained with Extra tree classifier algorithm by using feature selection technique along with bayesian optimization and hyperparameter tuning.