A Hybrid Model for Detection of Breast Cancer through Efficient Feature Selection using Machine Learning Approaches

Abstract

Cancer in breasts is considered as one of the dreaded diseases. It causes huge loss of human lives throughout the world and its menace is spreading fast. Earlier detection of breast cancer significantly enhances treatment effectiveness and patient’s prognosis. Traditional methods in many cases of diagnosis incur much expenses, time taking and prone to errors resulting demoralization and unsuccessful. Machine learning approaches have been showing promises in automating detection of cancers in breasts.  There exist a number of approaches in machine learning which show good results. This research tries to find out the techniques from the existing models and by addressing and modifying the underlying technical issues towards attaining higher accuracy. This study works using three individual learners namely, ‘Support Vector Machines, ‘Logistic Regression’ and ’Decision Trees’. Derives a hybrid learner from these three individual leaners .Using a comprehensive dataset obtained from clinical studies, available publicly online the proposed model applies Principal Component Analysis (PCA) for selecting features. This approach processes dataset, discern subtle patterns and enhances diagnostic accuracy reducing human errors. In the comparative analysis, it presents results of the model and evaluation of performance through metrics like accuracy, sensitivity and specificity. The model finally achieves 98.24% of accuracy in prediction which seems to be impressive in comparison to other existing models. The study upholds its potential as a significant tool in medical diagnostics.