An IoT-based Intelligent Wound Monitoring System

TL;DR

This paper presents an IoT-based intelligent wound monitoring system that uses decision trees with entropy and information gain to categorize wound healing status into three classes, enhancing assessment accuracy.

Contribution

The study introduces an IoT-enabled wound assessment system utilizing decision trees with entropy and information gain for improved wound status classification.

Findings

Decision tree with entropy and information gain improves classification accuracy.

Implementation in MATLAB demonstrates system feasibility.

Categorization into good, satisfactory, or alarming wound states.

Abstract

Clinical research of wound assessment focused on physical appearance of wound i.e. wound width, shape, color etc. Although, wound appearance is most crucial factors to influence healing process. however, apart from wound appearance other factors also contribute in healing process. Wound internal and external environment is one such factor that may show positive or negative impact on healing. Internet of things extensively popular during last decade, due to its heavy applications in almost all domains i.e. agriculture, health, marketing, banking, home etc. Therefore, in current research we proposed IoT based intelligent wound assessment system, for assessment of wound status and apply entropy and information gain statistics of decision tree to reflect status of wound assessment by categorization of assessment results in one of three class i.e. good, satisfactory or alarming. We implemented decision tree in MATLAB, in which we select ID3 algorithm for decision tree which based on entropy and information gain for the selection of best feature to split the tree. The efficient feature split of decision tree improved training accuracy rate and performance of decision tree.