ABSTRACT
The Internet of Health Things (IoHT) plays a vital role in the healthcare diagnosis process, which allows minimizing health-associated risk factors and medical expenses. At the same time, breast cancer is a crucial health issue that affects numerous women in the world. The detection of abnormal cells in the breast in the earlier stage can raise the survival rate of women. This chapter presents a new Internet of Things (IoT)–based Breast Cancer Diagnosis using a Hybrid Feature Extraction with Optimal Support Vector Machine–based Classification Model called the Hybrid Features-GA-SVM model. The proposed model involves a set of different stages namely image acquisition, preprocessing, hybrid feature extraction (HFE) process, and GA-SVM–based classification. The proposed model has been tested using a benchmark MIAS data set. The experimental outcome ensured that the Hybrid Features-GA-SVM model has resulted in a maximum sensitivity of 72.56%, a specificity of 78.34%, and an accuracy of 74.28%, respectively.
