INTERNET OF MEDICAL THINGS ENABLED CLOUD-BASED BREAST CANCER IDENTIFICATION WITH MACHINE LEARNING

Abstract

Breast cancer occurs when cells in the breast grow out of control. Breast cancer can spread outside the breast through lymph vessels and blood vessels when it spreads to other parts of the body, it is said to have metastasized. Most breast cancer cases are reported in women who are 50 years and/or o40 years older. According to facts and figures shared by WHO (World Health Organization), it impacts 2.1 million women every year and also causes the greatest number of cancer-related deaths amongst women. Whilst breast cancer rates are higher among women in more developed regions, rates are increasing in nearly every region globally. Different machine learning algorithms have been applied to the dataset like Naïve Bayes (NB), J48 Decision tree, K-Nearest Neighbor (KNN) and ANN (Gradient Descent) have been applied among them ANN (Gradient Descent) produces the optimal results among these classification algorithms. The proposed Internet of Medical Things Enabled Cloud-Based Breast Cancer Identification with Machine Learning system model with 98.07 % accuracy has been achieved. For the proposed model 97.64 % sensitivity and 98.32 % specificity have been recorded. From the results produced by the proposed expert system, it's satisfactory to utilize it for breast cancer diagnosis. The Proposed system model will be helpful for the diagnosis of breast cancer.