IoT based Illness Prediction System using Machine Learning

TL;DR: In this paper , the authors have proposed a novel framework for processing data with text-related properties using machine learning techniques and a data analysis process has been carried out by using a Machine Learning (ML) framework.

Abstract: The adoption of wearable technology will increase and its integration into daily life will improve, particularly in the healthcare sector. The emergence of mobile medicine, the development of new technologies like smart sensing, and the adoption of customised health ideas have all contributed to the rapid growth of smart wearable technology in recent years. The study was primarily focused on the use of wearable technology in office situations with the goal of daily health and safety monitoring of employees. In order to perform data classification and data labeling, a machine learning model is constructed. This research work has proposed a novel framework for processing data with text-related properties using machine learning techniques. Further a data analysis process has been carried out by using a Machine Learning (ML) framework. In the proposed study, machine learning classifiers are used. This study has analyzed the outcomes by considering accuracy as a performance indicator after applying the algorithms to the datasets. After analyzing the accuracy, it is evident that the machine learning algorithms like K-Nearest Neighbor (KNN) and Support Vector Machine (SVM) are effective on processing the text datasets.