Comparative Study of Machine Learning Algorithms using a Breast Cancer Dataset

Cancer, in general, is considered to be one of the highest causes of death worldwide. According to the Global Cancer statistics, breast cancer, which is the leading cause of death for women overall, is the second most diagnosed cancer with 11.6% of all positive cases. Whenever a lump of mass is found in the chest area, it would be diagnosed as either a cancerous or a non-cancerous tumor, which are also known as malignant or benign, respectively. Proper diagnosis is vital in order for the patient to start a treatment plan and recover as soon as possible. In this paper, we compare different Machine learning algorithms that are used to classify a patient's tumor using a set of features provided. Diagnostic Wisconsin Breast Cancer Dataset is used to train and test the different models which are then compared with each other using different classification metrics to identify the most robust and accurate models and compare against the state-of-the-art results.