An Intelligent-Mamdani Inference Scheme for Healthcare Applications Based on Fuzzy Markup Language
The experimental results show that the proposed Intelligent-Mamdani Inference Scheme (IMIS) is feasible for semantic decision of personal health and a person can understand his physical conditions via the generated semantic decision-marking mechanism by the input of vital signs.
Abstract
In recent years, personal health management has been interested to researchers and healthcare practitioners. Recording and analyzing physiological variations in ordinary life could be especially useful to manage health problems and to care individuals. It is widely pointed out that various vital signs are important indicators used to evaluate the wellness of physical bodies. In this study, an Intelligent-Mamdani Inference Scheme (IMIS) based on fuzzy markup language (FML) is proposed to apply to the semantic decision-making for personal health in healthcare applications. The IMIS could provide semantic analysis of personal health status by using the knowledge base and fuzzy inference rules, which are pre-established by domain experts. This scheme is a well-defined composition, including a FML editor, a FML parser, a fuzzy inference mechanism and a semantic decision-making mechanism. The experimental results show that the proposed scheme is feasible for semantic decision of personal health. A person can understand his physical conditions via the generated semantic decision-marking mechanism by the input of vital signs.