Optimizing Recurrent Neural Network-Based pH Prediction System of Halochromic Film for Chronic Wound Monitoring

Abstract

Chronic wounds, known for their prolonged healing and the associated financial strain on patients, highlight the growing need for effective monitoring methods. One approach to handling chronic wounds involves monitoring biomarkers, with pH emerging as a promising option. Halochromic sensors, designed to detect color changes in response to pH fluctuations, can be employed for monitoring pH variations in wounds. Previous studies have demonstrated the utility of halochromic-based materials in the field of wound management. Nevertheless, it is worth noting that data derived from such sensors may be prone to misinterpretation, particularly when subjected to human visual processing. Hence, this paper aims to develop a pH prediction system based on the fabricated polymer’s extracted values of different color channels. As a feature selection, principal component analysis (PCA) was performed to determine the number of significant features, and neighborhood component analysis (NCA) was conducted to eliminate the unrelated elements. Subsequently, an RNN model was developed and enhanced through the application of three distinct metaheuristic optimization algorithms: chernobyl disaster optimizer (CDO), chaos game optimizer (CGO), and coronavirus herd immunity optimization algorithm (CHIO). Following the simulation and evaluation of each optimized RNN model, the CGO-RNN model exhibited the lowest mean squared error (MSE) value of 0.1583, indicating that the RNN with chaos game optimizer stands out as the most effective model for this particular application.

Keywords

• Chernobyl disaster optimizer
• chaos game optimization
• coronavirus herd immunity optimizer
• halochromic film
• recurrent neural network
• metaheuristic algorithms