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A Study of Implementing Artificial Neural Networks and Cluster Analysis to Distinguish Fatigue Type and Level in Graduate Students

Published: 20 May 2017 Publication History

Abstract

There are many causes of fatigue and the prevalence rate and key factors of fatigue differ according to population groups. Masters students in Taiwan are a high risk group for fatigue, and their lifestyle data was collected as the subject of the present study. Physiological parameters are measured using mobile devices, and the checklist individual strength (CIS) questionnaire and fatigue type checklist are utilized to explore the prevalence rate and type of fatigue in masters students. Cluster analysis was used to establish fatigue levels, and Pearson's correlation analysis was used to explore the correlation between different fatigue types and fatigue level. The results obtained from the CIS questionnaire showed a fatigue prevalence rate of 50%, with a Cronbach's alpha value of 0.885, indicating good internal consistency. Fatigue type was established using the fatigue type checklist and a neural network. Masters students who are fatigued with an active sympathetic nervous system accounted for 28.75% of the subjects, while 21.75% of the subjects were fatigued with an active parasympathetic nervous system, where the key factors are the number of exercise days and the number of steps taken. Cluster analysis was then used to separate the degree of fatigue into four levels. Fatigue scores between 111 and 140 are classified as extremely fatigued; between 77 and 110 as generally fatigued; between 48 and 76 as borderline fatigued; and between 20 and 47 as removed from fatigue. Different fatigue levels were found to have different key factors, and the results of the present study can help provide differentiated solutions for different levels of fatigue.

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  1. A Study of Implementing Artificial Neural Networks and Cluster Analysis to Distinguish Fatigue Type and Level in Graduate Students

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    ICMHI '17: Proceedings of the 1st International Conference on Medical and Health Informatics 2017
    May 2017
    118 pages
    ISBN:9781450352246
    DOI:10.1145/3107514
    Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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    Published: 20 May 2017

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    Author Tags

    1. Fatigue prevalence rate
    2. cluster analysis
    3. fatigue level
    4. fatigue types
    5. neural networks

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