Development of a Machine Learning Model for the Management of Asthma

Abstract

Asthma management requires continuous monitoring of both physiological conditions and environmental triggers to prevent exacerbations and improve patient outcomes. Conventional inhalers, however, lack real-time monitoring and predictive capabilities, limiting their effectiveness in proactive healthcare. This study presents the design and implementation of a Smart Asthma Inhaler system that integrates sensor-based data acquisition, cloud computing, and machine learning (ML) for real-time monitoring and predictive risk assessment. The proposed system captures key parameters, including oxygen saturation (SpO 2 ), air quality index (AQI), temperature, humidity, and inhaler usage frequency. A Multiple Linear Regression model was employed to analyze the variables and generate predicted asthma risk scores, which were further categorized into risk levels for actionable feedback. Experimental evaluation was conducted using a pilot dataset collected from 20 asthma patients under varying environmental conditions. The system achieved an inhaler detection accuracy of 97.5%, with average data synchronization and prediction times of 2.3 and 2.7 seconds, respectively. The predictive model demonstrated strong performance with a coefficient of determination (R 2 ) of approximately 0.986, indicating high predictive accuracy. Non-functional evaluation further revealed high usability (4.5/5), scalability (handling up to 20 concurrent users), and reliability (94% uptime). The results demonstrate that the proposed system is efficient, accurate, and suitable for real-time asthma monitoring and prediction. The potential of integrating Internet of Things (IoT) and machine learning to enhance proactive healthcare and improve asthma management was highlighted.