Enny Rachmani1, Chien-Yeh Hsu2, Nurjanah Nurjanah3, Peter Wushou Chang4, Guruh Fajar Shidik5, Edi Noersasongko6, Jumanto Jumanto7, Anis Fuad8, Dina Nur Anggraini Ningrum9, Arif Kurniadi10, Ming-Chin Lin11. 1. Graduate Institute of Biomedical Informatics, College of Medical Science and Technology, Taipei Medical University 15F, No. 172-1, Sec. 2 Keelung Rd, Da'an District, Taipei City 106, Taiwan; Department of Health Information Management, Faculty of Health Science, Universitas Dian Nuswantoro, Jl. Nakula No 1-5, Semarang, Jawa Tengah, 50131, Indonesia. Electronic address: enny.rachmani@dsn.dinus.ac.id. 2. Department of Information Management, National Taipei University of Nursing and Health Science, No.365, Ming-te Road, Beitou District, Taipei City, Taiwan; Master Program in Global Health and Development, Taipei Medical University, No. 250 Wu-Xing Street, Xinyi District, Taipei City 101, Taiwan. Electronic address: cyhsu@ntunhs.edu.tw. 3. Department of Public Health, Faculty of Health Science, Universitas Dian Nuswantoro, Jl. Nakula No 1-5, Semarang, Jawa Tengah, 50131, Indonesia. Electronic address: nurjanah@dsn.dinus.ac.id. 4. Chung Shan Medical University, Taichung, Taiwan; Tufts University Medical School, Boston, USA. Electronic address: peter.chang3@gmail.com. 5. Department of Informatics, Faculty of Computer Science, Universitas Dian Nuswantoro, Jl. Nakula No 1-5, Semarang, Jawa Tengah, 50131, Indonesia. Electronic address: guruh.fajar@research.dinus.ac.id. 6. Department of Informatics, Faculty of Computer Science, Universitas Dian Nuswantoro, Jl. Nakula No 1-5, Semarang, Jawa Tengah, 50131, Indonesia. Electronic address: edi.noersasongko@dsn.dinus.ac.id. 7. Faculty of Humanities, Universitas Dian Nuswantoro, Jl. Imam Bonjol No 127, Semarang, Indonesia. Electronic address: ilhamj@dsn.dinus.ac.id. 8. Graduate Institute of Biomedical Informatics, College of Medical Science and Technology, Taipei Medical University 15F, No. 172-1, Sec. 2 Keelung Rd, Da'an District, Taipei City 106, Taiwan; Department of Biostatistics, Epidemiology and Population Health, Public Health, Faculty of Medicine, Gajah Mada University, Jl. Farmako Sekip Utara, Sinduadi, Mlati, Kabupaten Sleman, Yogjakarta 55281, Indonesia. Electronic address: anisfuad@ugm.ac.id. 9. Graduate Institute of Biomedical Informatics, College of Medical Science and Technology, Taipei Medical University 15F, No. 172-1, Sec. 2 Keelung Rd, Da'an District, Taipei City 106, Taiwan; Department of Public Health, Semarang State University, Semarang, Indonesia.Jalan Sekaran, Gunung Pati, Sekaran, Kota Semarang, Jawa Tengah 50229, Indonesia. Electronic address: dinanan@mail.unnes.ac.id. 10. Department of Health Information Management, Faculty of Health Science, Universitas Dian Nuswantoro, Jl. Nakula No 1-5, Semarang, Jawa Tengah, 50131, Indonesia. Electronic address: arif.kurniadi@dsn.dinus.ac.id. 11. Graduate Institute of Biomedical Informatics, College of Medical Science and Technology, Taipei Medical University 15F, No. 172-1, Sec. 2 Keelung Rd, Da'an District, Taipei City 106, Taiwan; Department of Neurosurgery, Shuang Ho Hospital, Taipei Medical University, No.291, Zhongzheng Rd., Zhonghe District, New Taipei City, 23561, Taiwan; Taipei Neuroscience Institute, Taipei Medical University, Taipei, Taiwan. Electronic address: arbiter@tmu.edu.tw.
Abstract
BACKGROUND AND OBJECTIVE: Measuring health literacy becomes more important because its association with health status and healthcare outcomes. Studies have developed at least 133 measurement tools for health literacy. HLS-EU-Q47 is a questionnaire consisting of 12 sub-dimensions and 47 questions developed by the Europe Health Literacy Consortium. Many countries in Europe and Asia have used HLS-EU-Q47 as a tool for measuring health literacy in the general public. Indonesia has conducted general health literacy survey using HLS-EU-Q47 but finding the difficulties because of the time-consuming interview. A shorter version of HLS-EU-Q47 is needed to apply in health literacy researches in Indonesia. This paper reports the results of feature reduction to develop a short Indonesian version HLS-EU questionnaire and measures the accuracy of the model compared with other short form like HLS-EU-SQ16 or HLS-SF12. METHOD: The analysis was performed on a population-based dataset from Indonesia-Semarang Health Literacy Survey for which there were specific target variables as the classification of health literacy level. All attributes were assessed as potential targets in the models derived from the full dataset and its subsets. The feature selection methods with genetic algorithm were used as the filter as well as validation (cross validation) and classification (k-NN:k-nearest neighbor). The predictive accuracy of health literacy level and the complexity of models based on the reduced datasets were compared among the methods and other short versions such as HLS-EU-SQ16, HLS-SF12. RESULT: The accuracy of the existing short form models were 90.64% with the HLS-EU-SQ16 and 88.67% with the HLS-SF12. This study proposed a model with 10 features as the construct of a short Indonesian-version (proposed as the HLS-EU-SQ10-IDN) since the model was with higher accuracy than the HLS-SF12, but fewer features for measuring general health literacy index. Moreover, the short version only completed part of 12 dimensions of the full questionnare. CONCLUSION: A data mining technique using feature selection with combination of genetic algorithm and k-NN algorithm was applied to develop a short version questionnaire and proved to have better accuracy, as compared with the short version developed by traditional statistical technique.
BACKGROUND AND OBJECTIVE: Measuring health literacy becomes more important because its association with health status and healthcare outcomes. Studies have developed at least 133 measurement tools for health literacy. HLS-EU-Q47 is a questionnaire consisting of 12 sub-dimensions and 47 questions developed by the Europe Health Literacy Consortium. Many countries in Europe and Asia have used HLS-EU-Q47 as a tool for measuring health literacy in the general public. Indonesia has conducted general health literacy survey using HLS-EU-Q47 but finding the difficulties because of the time-consuming interview. A shorter version of HLS-EU-Q47 is needed to apply in health literacy researches in Indonesia. This paper reports the results of feature reduction to develop a short Indonesian version HLS-EU questionnaire and measures the accuracy of the model compared with other short form like HLS-EU-SQ16 or HLS-SF12. METHOD: The analysis was performed on a population-based dataset from Indonesia-Semarang Health Literacy Survey for which there were specific target variables as the classification of health literacy level. All attributes were assessed as potential targets in the models derived from the full dataset and its subsets. The feature selection methods with genetic algorithm were used as the filter as well as validation (cross validation) and classification (k-NN:k-nearest neighbor). The predictive accuracy of health literacy level and the complexity of models based on the reduced datasets were compared among the methods and other short versions such as HLS-EU-SQ16, HLS-SF12. RESULT: The accuracy of the existing short form models were 90.64% with the HLS-EU-SQ16 and 88.67% with the HLS-SF12. This study proposed a model with 10 features as the construct of a short Indonesian-version (proposed as the HLS-EU-SQ10-IDN) since the model was with higher accuracy than the HLS-SF12, but fewer features for measuring general health literacy index. Moreover, the short version only completed part of 12 dimensions of the full questionnare. CONCLUSION: A data mining technique using feature selection with combination of genetic algorithm and k-NN algorithm was applied to develop a short version questionnaire and proved to have better accuracy, as compared with the short version developed by traditional statistical technique.