Volume 7, Issue 3 (2019)                   HEHP 2019, 7(3): 139-145 | Back to browse issues page

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Enayati E, Hassani Z, Moodi M. The Application of an Evolutionary Model using Fuzzy Logic on Health Literacy Data. HEHP. 2019; 7 (3) :139-145
URL: http://journals.modares.ac.ir/article-5-32742-en.html
1- Computer Sciences Department, Basic Sciences Faculty, University of Bojnord, Bojnord, Iran , e.enayati@ub.ac.ir
2- Computer Sciences Department, Basic Sciences Faculty, University of Kosar, Bojnord, Iran
3- “Social Determinant of Health Research Center” and “Health Education & Health Promotion Department, Health Faculty”, Birjand University of Medical Sciences, Birjand, Iran
Abstract:   (132 Views)
Aims: Health literacy (HL) is the main factor shows health literate level of people in a certain society. Discovering and understanding affective factors on HL level could lead experts to improve these factors in the target community. This study aimed to Health Literacy classification of population and find a major component with data mining approaches.
Instruments and Methods: In this paper, we have acquired more details about major factors on the health literacy level of target society by assessing evolutionary methods. We benefit of Particle Swarm Optimization (PSO) and KNN and fuzzy KNN algorithm for classification and use wrapper technique for feature selection by our model. Feature selection are done as weighted features and selects the most effective features of health literacy. Our proposed model evaluates a data set of Health Literacy by two classifiers with/without fuzzy logic. Applied data set is a real data gathered from a descriptive-analytic cross-sectional study on adult population include 2133 record with 74 attributes in 2016 at South Khorasan province. We have gained effective factors on HL level of the population according to regions and total population without using any statistical analysis tools with the lowest human interference by an evolutionary method.
Findings: Proposed model have found effective factors on the health literacy level of population in South Khorasan province. Results are obtained 92.02% accuracy for the total population and 97.99% for regions population.
Conclusion: Simulations demonstrate the evolutionary method is a suitable way for extracting results from health data sets and also shows the superiority of the proposed method.
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Received: 2019/04/7 | Accepted: 2019/05/8 | Published: 2038/01/19

References
1. Rademakers J, Heijmans M. Beyond reading and understanding: Health literacy as the capacity to act. Int J Environ Res Public Health. 2018;15(8).pii:E1676. [Link] [DOI:10.3390/ijerph15081676]
2. The Swedish National Institute of Public Health. Healthy ageing: A challenge for Europe. Stockholm: The Swedish National Institute of Public Health; 2006. [Link]
3. Institute of Medicine, Board on Neuroscience and Behavioral Health, Committee on Health Literacy. Health literacy: A prescription to end confusion. Kindig DA, Panzer AM, Nielsen-Bohlman L, editors. Washington DC: National Academies Press; 2004. [Link]
4. Vernon JA, Trujillo A, Rosenbaum SJ, DeBuono B. Low health literacy: Implications for national health policy. Report. Washington DC: Department of Health Policy, School of Public Health and Health Services, The George Washington University; 2007. [Link]
5. Eichler K, Wieser S, Brügger U. The costs of limited health literacy: A systematic review. Int J Public Health. 2009;54(5):313-24. [Link] [DOI:10.1007/s00038-009-0058-2]
6. Sørensen K, Pelikan JM, Röthlin F, Ganahl K, Slonska Z, Doyle G, et al. Health literacy in Europe: Comparative results of the European Health Literacy Survey (HLS-EU). Eur J Public Health. 2015;25(6):1053-8. [Link] [DOI:10.1093/eurpub/ckv043]
7. Tehrani Banihashemi SA, Amirkhani MA, Haghdoost AA, Alavian SM, Asgharifard H, Baradaran H, et al. Health literacy and the influencing factors: A study in five provinces of Iran. Strides Dev Med Educ. 2007;4(1):1-9. [Persian] [Link]
8. Hina S, Shaikh A, Abul Sattar S. Analyzing diabetes datasets using data mining. J Basic Appl Sci. 2017;13:466-71. [Link] [DOI:10.6000/1927-5129.2017.13.77]
9. Marinov M, Mosa AS, Yoo I, Boren SA. Data-mining technologies for diabetes: A systematic review. J Diabetes Sci Technol. 2011;5(6):1549-56. [Link] [DOI:10.1177/193229681100500631]
10. Fallah M, Niakan Kalhori SR. Systematic review of data mining applications in patient-centered mobile-based information systems. Healthc Inform Res. 2017;23(4):262-70. [Link] [DOI:10.4258/hir.2017.23.4.262]
11. Domadiya N, Rao UP. Privacy preserving distributed association rule mining approach on vertically partitioned healthcare data. Procedia Comput Sci. 2019;148:303-12. [Link] [DOI:10.1016/j.procs.2019.01.023]
12. Chaurasia V, Pal S, Tiwari BB. Prediction of benign and malignant breast cancer using data mining techniques. J Algorithms Comput Technol. 2018;12(2):119-26. [Link] [DOI:10.1177/1748301818756225]
13. Aljumah AA, Ahamad MG, Siddiqui MK. Application of data mining: Diabetes health care in young and old patients. J King Saud Univ Comput Inf Sci. 2013;25(2):127-36. [Link] [DOI:10.1016/j.jksuci.2012.10.003]
14. Senturk ZK, Kara R. Breast cancer diagnosis via data mining: performance analysis of seven different algorithms. Comput Sci Eng Int J. 2014;4(1):35-46. [Link] [DOI:10.5121/cseij.2014.4104]
15. Liao SH, Chu PH, Hsiao PY. Data mining techniques and applications - a decade review from 2000 to 2011. Expert Syst Appl. 2012;39(12):11303-11. [Link] [DOI:10.1016/j.eswa.2012.02.063]
16. Jamili Oskouei R, Moradi Kor N, Abbasi Maleki S. Data mining and medical world: Breast cancers' diagnosis, treatment, prognosis and challenges. Am J Cancer Res. 2017;7(3):610-27. [Link]
17. Jamili Oskouei R, Farokhbalaghi Sh. Data mining application for exploring the relationship between addiction and depression. Int J Comput Inf Technol. 2016;4(2):43-7. [Link]
18. Alonso SG, De La Torre-Díez I, Hamrioui S, López-Coronado M, Barreno DC, Nozaleda LM, et al. Data mining algorithms and techniques in mental health: A systematic review. J Med Syst. 2018;42(9):161. [Link] [DOI:10.1007/s10916-018-1018-2]
19. Ioniţă I, Ioniţă L. Applying data mining techniques in healthcare. Stud Inform Control. 2016;25(3):385-94. [Link] [DOI:10.24846/v25i3y201612]
20. Sengupta S, Basak S, Peters II RA. Particle swarm optimization: A survey of historical and recent developments with hybridization perspectives. Mach Learn Knowl Extr. 2018;1(1):157-91. [Link] [DOI:10.3390/make1010010]
21. Zhang Y, Wang Sh, Ji G. A comprehensive survey on particle swarm optimization algorithm and its applications. Math Probl Eng. 2015;2015:931256. [Link] [DOI:10.1155/2015/931256]
22. Kennedy J, Eberhart R. Particle swarm optimization. Proceedings of ICNN'95 International Conference on Neural Networks, 27 Nov-1 Dec 1995, Perth, WA, Australia. Piscataway: IEEE; 1995. [Link]
23. Salvador-Meneses J, Ruiz-Chavez Z, Garcia-Rodriguez J. Compressed kNN: K-nearest neighbors with data compression. Entropy. 2019;21(3):234. [Link] [DOI:10.3390/e21030234]
24. Fan GF, Guo YH, Zheng JM, Hong WC. Application of the weighted k-nearest neighbor algorithm for short-term load forecasting. Energies. 2019;12(5):916. [Link] [DOI:10.3390/en12050916]
25. Amirfakhrian M, Sajadi S. Fuzzy k-nearest neighbor method to classify data in a closed area. Int J Math Model Comput. 2013;3(2):109-14. [Link]
26. Castillo O, Melin P, Ramírez E, Soria J. Hybrid intelligent system for cardiac arrhythmia classification with fuzzy k-nearest neighbors and neural networks combined with a fuzzy system. Expert Syst Appl. 2012;39(3):2947-55. [Link] [DOI:10.1016/j.eswa.2011.08.156]
27. Singh Sh, Acharya SD, Kamath A, Ullal SD, Urval RP. Health literacy status and understanding of the prescription instructions in diabetic patients. J Diabetes Res. 2018;2018:4517243. [Link] [DOI:10.1155/2018/4517243]
28. Kandula S, Ancker JS, Kaufman DR, Currie LM, Zeng-Treitler Q. A new adaptive testing algorithm for shortening health literacy assessments. BMC Med Inform Decis Mak. 2011;11:52. [Link] [DOI:10.1186/1472-6947-11-52]

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