DESIGN AND ANALYSIS OF RANDOM FOREST ON RESOURCE OPTIMIZATION INTELLIGENT IOT SYSTEMS IN HEALTHCARE INDUSTRIAL ENVIRONMENTS

Abstract

Aim/Scope:  Emphasizing Intelligent Internet of Things (IoT) Systems notably, the paper evaluates the Random Forest algorithm in resource optimization in industrial settings related to healthcare. This project aims to improve the administration of data and the decision-making process in healthcare contexts by means of design, implementation, and analysis of an RF algorithm.

Background:  For real-time patient data collecting from many sites, IoT sensors and devices are growing in importance for healthcare systems. However, given its volume, variety, and speed, efficiently digesting this data and getting important insights creates challenges. Among other machine learning methods, Random Forest offers potential strategies for best use of resources and improvement of healthcare decision-making processes.

Methodology:  Compiling patient data from home, laboratory, clinic, remote location using IoT sensors from many sources. Data computation and categorization follow from Random Forest then. Experimental evaluations make use of several disease sets obtained from credible sources.

Contribution:  IoT is a network design allowing billions of physical objects to be connected to the internet and data sharing among themselves. This relates to the software as well as the numerous sensors forming the Internet of Things. Devices—physical as well as mobile ones and other types—are able to interact with one another using wireless communication technology. For data processing and classification needs, the Random Forest approach follows afterward. Experimental evaluations make advantage of a wide spectrum of disease datasets obtained from reliable sources.

Findings: Compared to other classification methods, such Decision Tree, which produces motivating accuracy, sensitivity, and specificity. The results of the conducted out experiments confirm this.

Recommendations for Researchers:  Future avenues of research could involve looking at the scalability and applicability of the Random Forest algorithm in different and diversified bigger healthcare systems and environs. Moreover, looking at ensemble methods and hybrid approaches combining RF algorithm can help to improve its performance and utilization in industrial environments related to healthcare. Moreover, the investigation of the effects of the RF algorithm on patient outcomes, cost-effectiveness, and resource use would give medical practitioners perceptive understanding.

Future Research: In future research, this work can be enhanced using several deep learning algorithms integrated with IoT technology for achieving better accuracy and performance.