A healthcare monitoring system using random forest and internet of things (IoT)

• Published in: Multimedia tools and applications Vol. 78; no. 14; pp. 19905 - 19916
• Main Authors: Kaur, Pavleen, Kumar, Ravinder, Kumar, Munish
• Format: Journal Article
• Language: English
• Published: New York Springer US 01.07.2019; Springer Nature B.V
• Subjects: Algorithms; Artificial intelligence; Breast cancer; Cloud computing; Computer Communication Networks; Computer Science; Data analysis; Data Structures and Information Theory; Decision making; Decision trees; Dermatology; Diabetes mellitus; Empirical analysis; Health care; Health services; Heart diseases; Information technology; Internet of Things; Liver; Machine learning; Mental health; Monitoring systems; Multimedia Information Systems; Predictions; Remote monitoring; Special Purpose and Application-Based Systems; Support vector machines; Systems analysis; Internet of things; Healthcare; Data mining; Machine learning
• DOI: 10.1007/s11042-019-7327-8
• ISSN: 1380-7501

The Internet of Things (IoT) enabled various types of applications in the field of information technology, smart and connected health care is notably a crucial one is one of them. Our physical and mental health information can be used to bring about a positive transformation change in the health care landscape using networked sensors. It makes it possible for monitoring to come to the people who don’t have ready access to effective health monitoring system. The captured data can then be analyzed using various machine learning algorithms and then shared through wireless connectivity with medical professionals who can make appropriate recommendations. These scenarios already exist, but we intend to enhance it by analyzing the past data for predicting future problems using prescriptive analytics. It will allow us to move from reactive to visionary approach by rapidly spotting trends and making recommendations on behalf of the actual medical service provider. In this paper, the authors have applied different machine learning techniques and considered public datasets of health care stored in the cloud to build a system, which allows for real time and remote health monitoring built on IoT infrastructure and associated with cloud computing. The system will be allowed to drive recommendations based on the historic and empirical data lying on the cloud. The authors have proposed a framework to uncover knowledge in a database, bringing light to disguise patterns which can help in credible decision making. This paper has evaluated prediction systems for diseases such as heart diseases, breast cancer, diabetes, spect_heart, thyroid, dermatology, liver disorders and surgical data using a number of input attributes related to that particular disease. Experimental results are conducted using a few machine learning algorithms considered in this paper like K-NN, Support Vector Machine, Decision Trees, Random Forest, and MLP.