Nowadays, Internet of Things (IoT) is changing eHealth and especially mobile Health (m-Health) systems. Currently, more and more fixed and mobile medical devices installed in patients’ personal body networks, medical devices, and the surrounding clinical/home environments collect and send a huge amount of heterogeneous health data to healthcare information systems for their analysis. In this context, machine learning and data mining techniques are becoming more and more important in many real-life problems. An important number of these techniques are dedicated to health data processing and analysis on mobile devices. Several mobile applications based on these techniques have emerged as an essential technology for improving the quality of medical diagnosis and treatments of many illnesses as well as many health disorders.

Existing techniques used for processing health data can be broadly classified into two categories: (a) non-Artificial Intelligence (AI) systems and (b) Artificial Intelligence systems. Even though non-AI techniques are less complex in nature, most of the systems suffer from the drawbacks of inaccuracy and lack of convergence. Hence, these systems are generally replaced by AI based systems which are much superior to the conventional systems. AI techniques are mostly hybrid in nature and include Artificial Neural Networks (ANN), fuzzy theory, and evolutionary algorithms. Though most of the techniques are theoretically sound, the potential of these techniques is not fully explored for practical applications. Many of the computational applications still depend on non-AI systems, which limit their practical usage.

This special issue especially focuses on the feasibility of machine learning and data mining techniques on practical mobile health applications. These practical mobile applications include biomedical and medical images processing and health management. This special issue serves for discovering the untold advantages of data science techniques for practical mobile health applications and also brings out solutions for many real-life problems through advanced theoretical and experimental approaches.

Potential topics include but are not limited to the following:

• Novel architectures for m-Health data analysis and processing
• Fuzzy approaches for mobile applications dedicated to health management
• Evolutionary algorithms for optimization methodologies for m-Health applications
• Medical-informatics applications using intelligence methodologies on mobile devices
• Applications of AI techniques in signal and image processing on mobile devices
• Mobile biomedical applications involving ANN, fuzzy theory, and so forth
• Data mining for health data processing and analysis on mobile devices
• Machine learning and deep learning for health-related mobile applications