Early Detection of Breast Cancer Using Machine Learning Techniques

Cancer is the second cause of death in the world. 8.8 million patients died due to cancer in 2015. Breast cancer is the leading cause of death among women. Several types of research have been done on early detection of breast cancer to start treatment and increase the chance of survival. Most of the studies concentrated on mammogram images. However, mammogram images sometimes have a risk of false detection that may endanger the patient’s health. It is vital to find alternative methods which are easier to implement and work with different data sets, cheaper and safer, that can produce a more reliable prediction. This paper proposes a hybrid model combined of several Machine Learning (ML) algorithms including Support Vector Machine (SVM), Artificial Neural Network (ANN), K-Nearest Neighbor (KNN), Decision Tree (DT) for effective breast cancer detection. This study also discusses the datasets used for breast cancer detection and diagnosis. The proposed model can be used with different data types such as image, blood, etc

A Novel Weighted Hierarchical Adaptive Voting Ensemble Machine Learning Method for Breast Cancer Detection

A novel Weighted Hierarchical Adaptive Voting Ensemble (WHAVE) machine learning (ML) method was developed for breast cancer detection. It was constructed using three individual ML methods based on Multiple-Valued Logic: Disjunctive Normal Form (DNF) rule based method, Decision Trees, Naïve Bays, and one method based on continuous representation: Support Vector Machines (SVM). Results were compared with other methods and show that the WHAVE method accuracy was noticeably higher than the individual ML methods tested. This paper demonstrates that the WHAVE method proposed outperforms all methods researched, and shows the advantage of using WHAVE method for ML in breast cancer detection