Research on Human Health States Assessment Model Based on AHP-EWM in Multiple Scenarios
To solve the problem of insufficient personalized monitoring in human health assessment methods and meet the demand for fine-grained health status assessment in different scenarios,a multi-scenario-based human health status assessment method is needed to achieve long-term automated monitoring.This study proposes a multi-scenario human health assessment model based on a combination of the Analytic Hierarchy Process(AHP)and Entropy Weight Method(EWM).First,health monitoring index data for the human body in four different scenarios,including exercise,rest,work/study,and recreation,are collected to construct the corresponding assessment index system.Then,the AHP and EWM weights are calculated for the assessment indicators,and the Quantum-behaved Particle Swarm Optimization(QPSO)algorithm is used to distribute the subjective and objective weights for the AHP and EWM to ensure the objectivity of the proportion of evaluation indicators.Finally,the human health state is assessed and quantified using the fuzzy comprehensive evaluation method,and the reliability and stability of the method are verified using actual monitoring data.The experimental results show that the composite scores of the proposed method under the four scenarios(exercise,rest,work/study,and recreation)are 63.78,59.83,58.71,and 59.21,respectively,indicating that the model has good accuracy and stability under different scenarios.The results of the physical state evaluation of the testers are analyzed,and some health suggestions are given.The model proposed in this study can comprehensively determine the health status of the human body under different scenarios and provide scientific health guidance.Thus,it provides a scientific basis for health management and disease prevention.
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