Machine Learning-Based Model Development in Mobile Health Applications and Device-Cloud Integration

Mobile Health (mHealth) uses mobile devices and wireless technology to support healthcare practices, enabling widespread access to health services. Recent advancements in machine learning (ML) have enhanced healthcare by improving disease diagnosis and monitoring. However, integrating and executing machine learning models, especially those based on image processing and deep learning, on mobile devices can be challenging due to limited processing power and storage capacity. This study describes the steps of developing an ML-based model and its integration into mobile devices and cloud environments. A skin disease predictor using the MobileNet architecture was developed as a use-case mHealth application. Techniques such as transfer learning, data augmentation, and focal loss were employed to enhance model performance. The mHealth model was then integrated into a mobile device and the cloud environment. The on-device model exhibited faster prediction times (average 108.3 ms) compared to the cloud-based model (average 1281.2 ms). While on-device deployment ensured data privacy and offline functionality, the cloud approach provided scalability and easier updates, but at the expense of latency and data security. By providing a comparative analysis, this work demonstrates the feasibility of integrating ML models into mHealth applications, emphasizing the importance of balancing performance, cost, and usability.