Feature selection and classification in breast cancer prediction using IoT and machine learning. (June 2021) - Gopal, V.Nanda; Al-Turjman, Fadi; Kumar, R.; Anand, L.; Rajesh, M.
Highlights: Random Forest and Multi-Layer Perceptron (MLP) classifier. The breast cancer features are generated using radial based function network. Experiments with the Random Forest model and MLP model. Abstract: Breast cancer (BC) is the most commonly found disease among women all over the world. The early diagnosis of breast cancer can potentially reduce the mortality rate and increase the chances of a successful treatment. Paper focuses on proposing a methodology to conduct early diagnosis of breast cancer using the Internet of Things and Machine Learning. The main objective of the paper is to explore the machine learning techniques in predicting breast cancer with IoT devices.Proposed classifier resulted in 98%, 97%, 96% and 98% of precision, recall, F_Measure and accuracy, respectively. The minimum error rate for the classifier have also been determined and found to be 34.21%, 45.82%8, 64.47% of Mean Absolute Error (MAR), Root Mean Square Error (RMSE) and Relative Absolute Error (RAE), respectively. It was evident through the obtained results that the MLP classifier yields a higher accuracy with a minimum error rate when compared to LR and RF.