ABSTRACT
It is crucial that Breast Cancer should be detected early. Breast cancer time series forecasting is a novel data - driven approach to breast cancer diagnosis. Instead of looking at static images of the medical records, it analyses the dynamics in the tumour's growth rate, especially in its early stages. It uses machine learning models to find patterns that are not readily observable in static images, but are predictive of later outcomes. During COVID-19 it is necessary to monitor patient from home and IOT devices can be used that give moment forecast to the client and doctor during their typical day by day routine. Various Machine learning models are reviewed for classification of Breast Cancer symptoms. It is observed that data visualization and feature engineering play a crucial role in the classification before applying any model on data set. For human protection during COVID-19 it is better to depend on IoT enabled wearable device for automatic detection and appointment. The IoT enabled devices can use power of cloud computing and machine learning models to complete the framework of getting treated at home. Security of the data is another aspect to be taken into consideration. Solutions are available for the whole process and their aggregation will result in generating the desired model. In this paper, model is proposed to diagnose breast cancer at home using IoT, Blockchain, Machine learning and Cloud Computing. © 2022 American Institute of Physics Inc.. All rights reserved.