Prediction of breast cancer using voting classifier technique

Breast cancer became one of the deadliest cancer in women. It occurs when the growth of the cells in breast tissue become out of control. Cells are the building blocks for the organs and tissues in the body. When the growth of new cells are uncontrolled then they build-up mass of tissue called tumor. The tumors are categorized in to benign and malignant tumors. Early diagnosis needs an accurate diagnosis procedure that can be used by physicians to classify whether the tumor is benign or malignant. Classification algorithms are used to classify whether it is benign or malignant tumor. The main objective of this paper is to compare the results of supervised learning classification algorithms and combination of these algorithms using voting classifier technique. Voting is one of the ensemble approach where we can combine multiple models for the better classification. The dataset is taken from Wisconsin University database.