Effective Auto Data Processing Approach Based On Combined ML And CNN For Breast Cancer

In current era, there are various auto intelligent approaches to perform enormous activities related to healthcare as it needs. As learning concept plays a vital role to perform certain activities like learning the data’ s obtained and monitoring that patient behaviour in healthcare. As patient is concern, the heterogeneity problem occurs during the process of data extraction and prediction and also it overcome the problem of improper data accessibility as it needs to be mitigated for the patient’ s data related to healthcare. Here In order to better evaluate patient monitoring using patient care indicators like cost and staying length, we present an automated approach that integrates AI and IoT while also taking into consideration the challenge of heterogeneity. As the problem of heterogeneity is considered in the data processing to extract the data through certain set of rules to form another representation of data. Based on the pre-treatment information, post-treatment can be monitoring based on the prediction phase through modified Combined Random Forest and Convolutional Neural Network Algorithm to discuss how the effective prediction helps the doctor helps to processes in process treatment by deploying the automated framework with the integration of AI and IoT for real time health monitoring system. Here we have integrated the three phases process namely, pre-treatment, in treatment and post treatment. Here the breast cancer patient is considered, as it takes numerous numbers of small information’ s as it helps the patient to stay for longer time with longer treatment, which needs proper diagnosis for the treatment and improve the cost and staying possibilities parameters. In the performance analysis, history of the preference of the specific data and retrieve those data effectively based on cost and resource parameters to monitor health of the patient. Here the validation of the framework evaluation based on scalability and reliability improvement.