Reducing the Electronic Nose Sensor Array for Asthma Detection Using Firefly Algorithm

Exhaled breath analysis comprises chemical compounds that can be utilized for diagnostic purposes, including asthma detection. An electronic nose can be offered as a means of monitoring patient circumstances. A significant problem often occurs when determining the appropriate number of gas sensors while maintaining high accuracy. The firefly algorithm (FA) is very effective because of its exploratory capabilities, presents theories that are easy to understand and has relatively fewer parameters. This study aims to reduce and determine the appropriate number of gas sensors for an electronic nose in differentiating healthy and asthmatic subjects using the FA and exhaled breath analysis. The experimental results indicate that the FA provides only four gas sensors that still maintain high performance. The convolutional neural network model was favored for its ability to classify the entire asthma dataset, making it the best machine learning model for the electronic nose, with an accuracy of 97.8%.