Smart Sensors for Industry 4.0

1IoT-Based Health Monitoring Using a Hybrid Machine Learning Model

Shiplu Das¹, Gargi Chakraborty¹, Debarun Joardar¹, Subrata Paul¹, Buddhadeb Pradhan²

¹ Department of Computer Science and Engineering, Brainware University, Kolkata, India

² Department of Computer Science and Engineering, University of Engineering and Management

(Computer Science and Engineering), Kolkata, India

Email: [e-mail], [e-mail], [e-mail], [e-mail], [e-mail]

Abstract

This chapter proposes a hybrid model for IoT-based health monitoring using machine learning. The model combines various machine learning algorithms, including random forest, K-nearest neighbors, support vector machines, and neural networks, to predict the health status of patients based on various physiological parameters. The proposed model was trained and evaluated on a publicly available dataset of ICU patients, which contains physiological measurements such as heart rate, blood pressure, and saturation of oxygen, as well as demographic information such as age and gender. The random forest and neural network models were found to be particularly effective in predicting the health status of patients. The proposed hybrid model has several practical applications in healthcare, such as early warning systems for critical care patients and remote patient monitoring systems for chronic diseases. By using machine learning to predict the health status of patients ...