<doi_batch xmlns="http://www.crossref.org/schema/4.4.0" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" version="4.4.0"><head><doi_batch_id>2b7cd458-0d45-4214-9580-e738e8d90ea2</doi_batch_id><timestamp>20230119092304080</timestamp><depositor><depositor_name>wseas:wseas</depositor_name><email_address>mdt@crossref.org</email_address></depositor><registrant>MDT Deposit</registrant></head><body><journal><journal_metadata language="en"><full_title>WSEAS TRANSACTIONS ON BIOLOGY AND BIOMEDICINE</full_title><issn media_type="electronic">2224-2902</issn><issn media_type="print">1109-9518</issn><archive_locations><archive name="Portico"/></archive_locations><doi_data><doi>10.37394/23208</doi><resource>http://wseas.org/wseas/cms.action?id=4011</resource></doi_data></journal_metadata><journal_issue><publication_date media_type="online"><month>1</month><day>5</day><year>2022</year></publication_date><publication_date media_type="print"><month>1</month><day>5</day><year>2022</year></publication_date><journal_volume><volume>19</volume><doi_data><doi>10.37394/23208.2022.19</doi><resource>https://wseas.com/journals/bab/2022.php</resource></doi_data></journal_volume></journal_issue><journal_article language="en"><titles><title>Development of a Healthcare Monitoring Diabetes Mobile Application for Community</title></titles><contributors><person_name sequence="first" contributor_role="author"><given_name>Kanyarat</given_name><surname>Bussaban</surname><affiliation>Faculty of Science and Technology Suan Sunandha Rajabhat University Bangkok, THAILAND</affiliation></person_name><person_name sequence="additional" contributor_role="author"><given_name>Kanyarat</given_name><surname>Bussaban</surname><affiliation>Computer Science Program Suan Sunandha Rajabhat University Bangkok, THAILAND</affiliation></person_name><person_name sequence="additional" contributor_role="author"><given_name>Nareenart</given_name><surname>Ruksuntorn</surname><affiliation>Robotics Engineering program Faculty of Industrial Technology Suan Sunandha Rajabhat University Bangkok, THAILAND</affiliation></person_name><person_name sequence="additional" contributor_role="author"><given_name>Jaruwan</given_name><surname>Chutrtong</surname><affiliation>Faculty of Science and Technology Suan Sunandha Rajabhat University Bangkok, THAILAND</affiliation></person_name><person_name sequence="additional" contributor_role="author"><given_name>Chanyapat</given_name><surname>Sangsuwan</surname><affiliation>Faculty of Science and Technology Suan Sunandha Rajabhat University Bangkok, THAILAND</affiliation></person_name></contributors><jats:abstract xmlns:jats="http://www.ncbi.nlm.nih.gov/JATS1"><jats:p>The purpose of this project is to develop the mobile application, by applied Machine learning, for analyzing, collecting, monitoring, and retrieving information between patients with diabetes especially diabetes type 2 and village public health volunteers and to study the impact of using mobile application based on self- learning and self-management in diabetes information. This is a research and development mobile application and the sample consisted of 30 diabetes patients and 5 village health volunteers participated in this research. The project has demonstrated the effectiveness of using mobile application to support patients and village health volunteers. The results showed that user satisfaction has a high level.</jats:p></jats:abstract><publication_date media_type="online"><month>12</month><day>31</day><year>2022</year></publication_date><publication_date media_type="print"><month>12</month><day>31</day><year>2022</year></publication_date><pages><first_page>222</first_page><last_page>225</last_page></pages><publisher_item><item_number item_number_type="article_number">24</item_number></publisher_item><ai:program xmlns:ai="http://www.crossref.org/AccessIndicators.xsd" name="AccessIndicators"><ai:free_to_read start_date="2022-12-31"/><ai:license_ref applies_to="am" start_date="2022-12-31">https://wseas.com/journals/bab/2022/a485108-020(2022).pdf</ai:license_ref></ai:program><archive_locations><archive name="Portico"/></archive_locations><doi_data><doi>10.37394/23208.2022.19.24</doi><resource>https://wseas.com/journals/bab/2022/a485108-020(2022).pdf</resource></doi_data><citation_list><citation key="ref0"><unstructured_citation>Type 2 diabetes: https://www.mayoclinic.org/diseasesconditions/type-2-diabetes/symptoms-causes/syc-20351193 </unstructured_citation></citation><citation key="ref1"><doi>10.3390/ijerph17072329</doi><unstructured_citation>Tunsuchart K, Lerttrakarnnon P, Srithanaviboonchai K, Likhitsathian S, Skulphan S. Type 2 Diabetes Mellitus Related Distress in Thailand. Int J Environ Res Public Health. 2020 Mar 30;17(7):2329. doi: 10.3390/ijerph17072329. PMID: 32235629; PMCID: PMC7177402. </unstructured_citation></citation><citation key="ref2"><unstructured_citation>Thailand, Diabetes report 200-2045: https://diabetesatlas.org/data/en/country/196/th.html </unstructured_citation></citation><citation key="ref3"><unstructured_citation>B. Sanguanprasit, P. Leerapan, and P. Taechaboonsermsak, “Village health volunteers as peer supports for glycemic control among type 2 diabetes patients in Thailand”, J Public Hlth Dev, vol. 14, no. 2, pp. 49–60, Sep. 2016. </unstructured_citation></citation><citation key="ref4"><doi>10.3390/s22051843</doi><unstructured_citation>Makroum MA, Adda M, Bouzouane A, Ibrahim H. Machine Learning and Smart Devices for Diabetes Management: Systematic Review. Sensors (Basel). 2022 Feb 25;22(5):1843. doi: 10.3390/s22051843. PMID: 35270989; PMCID: PMC8915068. </unstructured_citation></citation><citation key="ref5"><doi>10.12659/msm.926719</doi><unstructured_citation>Zhai Y, Yu W. A Mobile App for Diabetes Management: Impact on Self-Efficacy Among Patients with Type 2 Diabetes at a Community Hospital. Med Sci Monit. 2020 Nov 16;26:e926719. doi: 10.12659/MSM.926719. PMID: 33196634; PMCID: PMC7678242. </unstructured_citation></citation><citation key="ref6"><doi>10.1016/j.pcd.2021.07.004</doi><unstructured_citation>Represas-Carrera F.J., Martínez-Ques Á.A., Clavería A. Effectiveness of mobile applications in diabetic patients’ healthy lifestyles: A review of systematic reviews. Prim. Care Diabetes. 2021;15:751–760. doi: 10.1016/j.pcd.2021.07.004. </unstructured_citation></citation><citation key="ref7"><doi>10.1007/s42600-021-00150-7</doi><unstructured_citation>de Araújo, W.R.V.C., Martins, L.E.G. &amp; Zorzal, E.R. Mobile apps for the treatment of diabetes patients: a systematic review. Res. Biomed. Eng. 37, 273–288 (2021). https://doi.org/10.1007/s42600-021-00150- 7 </unstructured_citation></citation><citation key="ref8"><doi>10.1136/bmjopen-2020-047556</doi><unstructured_citation>Dike FO, Mutabazi JC, Ubani BC, Isa AS, Ezeude C, Musa E, Iheonye H, Ainavi II. Implementation and impact of mobile health (mHealth) in the management of diabetes mellitus in Africa: a systematic review protocol. BMJ Open. 2021 Dec 17;11(12):e047556. doi: 10.1136/bmjopen-2020-047556. </unstructured_citation></citation><citation key="ref9"><doi>10.1016/j.cmpbup.2021.100002</doi><unstructured_citation>Alaa A. Abd-alrazaq, Noor Suleiman, Khaled Baagar, Noor Jandali, Dari Alhuwail, Ibrahem Abdalhakam, Saad Shahbal, Abdul-Badi Abou-Samra, Mowafa Househ, Patients and healthcare workers experience with a mobile application for self-management of diabetes in Qatar: A qualitative study, Computer Methods and Programs in Biomedicine Update, Volume 1, 2021,100002,ISSN 2666- 9900,https://doi.org/10.1016/j.cmpbup.2021.100002. </unstructured_citation></citation><citation key="ref10"><doi>10.1111/j.1365-2753.2007.00881.x</doi><unstructured_citation>Faridi Z, Liberti L, Shuval K, Northrup V, Ali A, Katz DL. Evaluating the impact of mobile telephone technology on type 2 diabetic patients’ selfmanagement: the NICHE pilot study. J Eval Clin Pract. 2008;14(3):465–9 </unstructured_citation></citation><citation key="ref11"><doi>10.2196/mhealth.3882</doi><unstructured_citation>Holmen H, Torbørnsen A, Wahl AK, Jenum AK, Smastuen MC, Arsand E,et al. A mobile health intervention for self-management and lifestyle change for persons with type 2 diabetes, part 2: one-year results from the Norwegian Randomized Controlled Trial RENEWING HEALTH. JMIR Mhealth Uhealth. 2014;2(4):e57 </unstructured_citation></citation><citation key="ref12"><doi>10.1089/dia.2019.0086</doi><unstructured_citation>Baptista S, Trawley S, Pouwer F, Oldenburg B, Wadley G, Speight J. What do adults with type 2 diabetes want from the “Perfect” App? Results from the second diabetes MILES: Australia (MILES-2) study. Diabetes Technol Ther. 2019. https://doi.org/10.1089/dia.2019.0086. </unstructured_citation></citation><citation key="ref13"><doi>10.2196/mhealth.7263</doi><unstructured_citation>Boyle L, Grainger R, Hall RM, Krebs JD. Use of and beliefs about mobile phone apps for diabetes self-management: surveys of people in a hospital diabetes clinic and diabetes health professionals in New Zealand. JMIR mHealth uHealth. 2017;5(6):e85. </unstructured_citation></citation><citation key="ref14"><doi>10.1186/s13098-019-0480-4</doi><unstructured_citation>Jeffrey B, Bagala M, Creighton A, Leavey T, Nicholls S, Wood C, Longman J, Barker J, Pit S. Mobile phone applications and their use in the self-management of Type 2 Diabetes Mellitus: a qualitative study among app users and non-app users. Diabetol Metab Syndr. 2019 Oct 16;11:84. doi: 10.1186/s13098-019-0480-4. PMID: 31636719; PMCID: PMC6794726. </unstructured_citation></citation><citation key="ref15"><doi>10.1186/s12911-017-0493-6</doi><unstructured_citation>Petersen M and Hempler N (2017) Development and testing of a mobile application to support diabetes self-management for people with newly diagnosed type 2 diabetes: a design thinking case study. BMC Med Inform Decis Mak 17: 91. </unstructured_citation></citation><citation key="ref16"><doi>10.1016/j.csbj.2016.12.005</doi><unstructured_citation>Kavakiotis I., Tsave O., Salifoglou A., Maglaveras N., Vlahavas I., Chouvarda I. Machine learning and data mining methods in diabetes research. Comput. Struct. Biotechnol. J. 2017;15:104–116. doi: 10.1016/j.csbj.2016.12.005. </unstructured_citation></citation><citation key="ref17"><doi>10.1109/wiecon-ece.2017.8468885</doi><unstructured_citation>N. S. Khan, M. H. Muaz, A. Kabir and M. N. Islam, "Diabetes Predicting mHealth Application Using Machine Learning," 2017 IEEE International WIE Conference on Electrical and Computer Engineering (WIECON-ECE), 2017, pp. 237-240, doi: 10.1109/WIECONECE.2017.8468885. </unstructured_citation></citation><citation key="ref18"><doi>10.1109/ic2ie53219.2021.9649235</doi><unstructured_citation>C. G. Estonilo and E. D. Festijo, "Development of Deep LearningBased Mobile Application for Predicting Diabetes Mellitus," 2021 4th International Conference of Computer and Informatics Engineering (IC2IE), 2021, pp. 13-18, doi: 10.1109/IC2IE53219.2021.9649235. </unstructured_citation></citation><citation key="ref19"><doi>10.1136/bmjopen-2019-034723</doi><unstructured_citation>Aguilera A, Figueroa CA, Hernandez-Ramos R, et almHealth app using machine learning to increase physical activity in diabetes and depression: clinical trial protocol for the DIAMANTE StudyBMJ Open 2020;10:e034723. doi: 10.1136/bmjopen-2019-034723 </unstructured_citation></citation><citation key="ref20"><doi>10.4239/wjd.v8.i2.80</doi><unstructured_citation>Katigari MR, Ayatollahi H, Malek M, Haghighi MK. Fuzzy expert system for diagnosing diabetic neuropathy. World J Diab. (2017) 8:80. doi: 10.4239/wjd.v8.i2.80 </unstructured_citation></citation><citation key="ref21"><doi>10.1016/j.jksuci.2020.06.013</doi><unstructured_citation>Jyotismita Chaki, S. Thillai Ganesh, S.K Cidham, S. Ananda Theertan, Machine learning and artificial intelligence based Diabetes Mellitus detection and self-management: A systematic review, Journal of King Saud University - Computer and Information Sciences, Volume 34, Issue 6, Part B, 2022, Pages 3204-3225, ISSN 1319-1578, https://doi.org/10.1016/j.jksuci.2020.06.013. </unstructured_citation></citation><citation key="ref22"><unstructured_citation>https://archive-beta.ics.uci.edu/ </unstructured_citation></citation><citation key="ref23"><doi>10.18208/ksdc.2017.23.2.669</doi><unstructured_citation>Żaneta Lenczewska, UX and UI design in mobile app development process, https://pagepro.co/blog/ux-and-ui-design-in-mobile-app/ </unstructured_citation></citation><citation key="ref24"><doi>10.18178/ijiet.2019.9.8.1264</doi><unstructured_citation>K. Kularbphettong, U. Ampant, and N. Kongrodj, “An automated hydroponics system based on mobile application,” International Journal of Information and Education Technology, vol. 9, no. 8, pp. 548–552, 2019. + </unstructured_citation></citation><citation key="ref25"><unstructured_citation>Ron Kohavi; Foster Provost (1998). “Glossary of terms”. Machine Learning 30: 271–274. </unstructured_citation></citation><citation key="ref26"><unstructured_citation>N. Cheung, "Machine learning techniques for medical analysis," School of Information Technology and Electrical Engineering, B.Sc. Thesis, University of Queenland, 2001. </unstructured_citation></citation><citation key="ref27"><unstructured_citation>Cortes, C., Vapnik, V.: Support-Vector Networks. ML 20(3), 273– 297 (1995) Google Scholar</unstructured_citation></citation></citation_list></journal_article></journal></body></doi_batch>