TITLE:
A Healthcare System for Internet of Things (IoT) Application: Machine Learning Based Approach
AUTHORS:
M. Mamun-Ibn-Abdullah, M. Humayun Kabir
KEYWORDS:
Internet of Things (IoT), Healthcare, ECG Signal, Right Bundle Branch Block (RBBB)
JOURNAL NAME:
Journal of Computer and Communications,
Vol.9 No.7,
July
27,
2021
ABSTRACT: Internet of things (IoT) has become an interesting topic in the field of technological research. It is basically interconnecting of devices with each other over the internet. Beside its general use in terms of autonomous cars and smart homes, but some of the best applications of IoT technology in fields of health care monitoring is worth mentioning. The main purpose of this research work is to provide comport services for patients. It can be used to promote basic nursing care by improving the quality of care and patient safety from patient home environment. Rural area of a country lacks behind the proper patient monitoring system. So, remote monitoring and prescribing by sharing medical information in an authenticated manner is very effective for betterment of medical facilities in rural area. We have proposed a healthcare system which can analyze ECG report using supervise machine learning techniques. Analyzing report can be stored in cloud platform which can be further used to prescribe by the experienced medical practitioner. For performance evaluation, ECG data is analyzed using six supervised machine learning algorithms. Data sets are divided into two groups: 75 percent data for training the model and rest 25 percent data for testing. To avoid any kind of anomalies or repetitions, cross validation and random train-test split was used to obtain the result as accurate as possible.