Health risk analysis system for family caregiver of disabled person

Kritsanaphuti, Chalaruk; Lawang, Wannarat; Suksawatchon, Ureerat; Suksawatchon, Jakkarin

doi:10.1109/incit.2017.8257871

Cited by 1 publication

(4 citation statements)

References 12 publications

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“…From our previous work [2], we found that the size of one or more classes (risk levels) are much more greater than the other classes as shown in Table 3. Thus RAC model tends to majority classes while the data from the minority classes may be incorrectly classified.…”

Section: 1 Annotated Data Preparationmentioning

confidence: 81%

“…The summary results for our experiments are shown in Tables 5. This table presents the accuracies from previous work [2] before combining with the SCUT algorithm, and the accuracies after combining with the SCUT algorithm to deal with class imbalance problem. Table 5 shows the health risk prediction experiments performed in three health datasets and displays the best predictive accuracies associated with each of health datasets, for each of five the classification algorithms.…”

Section: Resultsmentioning

confidence: 99%

“…The caregiver caring is also important for long-term care. Because the caring for a person with disabilities is difficult and hard tasks to provide home care services every day [2]. The effect is that most family caregivers suffer health problems and require support from the people around them.…”

Section: Introductionmentioning

confidence: 99%

“…Therefore, the assessment of the health risks of the family caregiver is essential. Because the assessment of the factors can indicate the risks of family caregivers in case of health problems and can lead to activities for supporting healthcare [2].…”

Section: Introductionmentioning

confidence: 99%

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Health Risk Analysis Expert System for Family Caregiver of Person with Disabilities using Data Mining Techniques

Suksawatchon

Lawang

2018

ECTI-CIT

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The nursing care for the family caregiver of the disabled person is an important task for long-term care, since the caring people with disabilities is the difficult and hard task. In this paper, the Health Risk Analysis System or HRAS is introduced which is the new expert system for identifying the health risk level in three aspects including mental, physical, and social health aspects, and provides the intervention according to the health risk level of each aspect as well. The HRAS is the client-server system. The HRAS client proceeds on web-based application to collect health data via online questionnaire and shows the analysis results. The collected health data are transmitted to the server to analysis and to assess the health risk level by using the proposed classifier model named Risk Analysis Classifier or RAC. The classification algorithm and rule-based classifier are used to build the RAC. The RAC is evaluated using k-fold cross validation and the experts with annotated health data and unseen data. The evaluation results showed that Neural Network performs the best performance overall which it achieves the accuracy above 90% in all health data sets. Thus, the Neural Network is the most suitable classifier for this work. In addition, the HRAS has been deployed and collected the user experience via the formal survey. These survey results demonstrated that the system provides high accuracy assessment and very utilization in several aspects.

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Section: 1 Annotated Data Preparationmentioning

confidence: 81%

Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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