An Ensemble Model for Breast Cancer Prediction Using Sequential Least Squares Programming Method (SLSQP)

Cancer is a collection of diseases which is obsessed by uncontrolled growth of cells in the body to produce abnormal cells. Breast cancer is the most common cancer among women. In cancer, abnormal cells spread in other body part that is advance stage of cancer; at this stage survival rate is very low. So, Cancer detection at early stage is highly required to reduce the mortality rate. Here, Mortality means death because of cancer. With the improvement of innovation and machine learning techniques, cancer detection has made advances. Machine learning (ML) allows system to learn on the basis of past experiences and take decision using various statistical and probabilistic methods with minimal human intervention. This research work exhibited an ensemble model to predict the breast cancer using four machine learning techniques which are Support Vector Machine, Logistic Regression, Decision Tree and K-Nearest Neighbour. Results shows that proposed ensemble framework is more accurate in contrast of tradition single classification system. In this research work, SLSQP method is used to assign weight to each classification model and prediction of each classifier is combined using soft voting technique.