Intelligent Health Informatics with Personalisation in Weather-Based Healthcare Using Machine Learning

Enhancing personalisation is important for productive collaboration between humans and machines. This is because the integration of human intelligence with cognitive computing would provide added value to healthcare. While the well-being and human health can be profoundly affected by weather, the effect of machine learning on personalised weather-based healthcare for self-management is unclear. This paper seeks to understand how machine learning use affects the personalisation of weather-based healthcare. Based on the Uses and Gratifications Theory (UGT), new constructs are incorporated (demography, weather and effectiveness) in order to propose a model for health science with machine learning use, weather-based healthcare, and personalisation. Subsequently, this paper proposes building a system that can predict the symptoms of two diseases (asthma and eczema) based on weather triggers. The outcome from this paper will provide deeper understanding of how personalisation is impacted by machine learning usage and weather-based healthcare for individual patients’ self-management and early prevention. The findings in this paper will also assist machine learning facilitators design effective use policies for weather-based healthcare that will have new fundamental knowledge with personalisation to enhance the future of intelligent health informatics, and artificial intelligence.