Publication

Abstract : In the healthcare systems, usage of advanced integrated technologies like Internet of things (IoT) and Machine learning (ML) techniques were limited. Different amalgams of IoT devices and ML mechanisms are available for medical sector but are limited to certain domains only. These models either provide patients current state data or specific domain analyzing and surveillance pre/post treatment data like heart or brain functions with corresponding medical aid. Also data available is used as clinical study for medical professionals and for better understandings of patients about their state. Specific domain gadgets' like wrist bands or smart bands uses some sensors about vital, temperature and pulse etc., checkups are available but they were not meant for diagnosing or for treatments. In this paper, we proposed an integrated model to use IoT and ML algorithms for a healthcare system. Tracking of patients' status can be done using some sensors such as lightweight, portable, and low-powered sensor nodes. These Sensors sense the patient's status and send the parametric data to the central controller, to take actions during the critical condition of the patients. The data sent to the controller always provided in secure and encryption form. At the same time, patient data is sent to doctors, so that they can provide the instructions to the caretakers of the patients with quick and proper solutions in real-time. For disease prediction, our model uses supervised machine learning algorithms, In order to get the efficient feature set and improve the better accuracy, and pre-processing techniques to eliminate features that are irrelevant, missing values and outliers from biomedical data which aids in better disease prediction.