Framework for Breast Cancer Diagnosis Using Machine Learning and IoT

Breast cancer is the most frequent malignancy discovered in women across the world. The early and accurate diagnosis of breast cancer is critical for lowering the mortality rate and raising the odds of successful therapy. The goal of this paper is to provide a technique for conducting early breast cancer diagnosis via machine learning and IoT. The main aim of the paper is to provide an alternative to the conventional diagnosis technique by using several machine learning algorithms. Breast cancer diagnosis using machine learning is a non-invasive technique with high accuracy rate. The proposed technique showed accuracy of 92.98 percent, 96.49 percent,97.36 percent, and 98.24 percent using the decision tree, random forest, logistic regression, and eXtreme gradient boosting algorithms, respectively. It was evident through the obtained results that the eXtreme gradient boosting yields the highest accuracy.