A feature selection using improved dragonfly algorithm with support vector machine for breast cancer prediction

Abstract

ABSTRACT Breast cancer is the most common cancer-related death in women, accounting for 16% of all cancer-related fatalities globally. Breast cancer is fatal in just half of all cases. Radiologists may misread worrisome lesions due to imaging quality concerns and diverse breast densities, which raises the false-(positive and negative) ratio, as the primary explanation for the problem. Early intervention is critical in building a current prognosis process that can successfully limit disease consequences and increase recovery. Patients are being referred back for biopsies to dispel suspicions when the inconsistent feature-extraction approach is used for manual screening of breast abnormalities in traditional schemes. With the Multi-kernel Support Vector Machine (MKSVM), we develop a new modality for the prediction of breast cancer and train its properties using the supervised machine learning approaches. Furthermore, the IDA-MKSVM technique achieved maximum average accuracy is 95.75%, average sensitivity is 94.29%, average specificity is 95.16% and average F-score is 95.39% for different training datasets. For selecting ideal features, the Improved Dragonfly Algorithm (IDA) is also used. A 10-fold cross validation procedure is used in the system under consideration to ensure accuracy. The UCI machine learning repository holds the breast cancer diagnosis data referred to as the Wisconsin breast cancer diagnosis data set.