DEVELOPING MACHINE LEARNING APPLICATION FOR EARLY CARDIOVASCULAR DISEASE (CVD) RISK DETECTION IN FIJI: A DESIGN SCIENCE APPROACH
Shahil SHARMA
shahil.sharma@fnu.ac.fjFiji National University (Fiji)
Rajnesh LAL
Fiji National University (Fiji)
https://orcid.org/0000-0002-3034-9751
Bimal KUMAR
Fiji National University (Fiji)
https://orcid.org/0000-0003-3622-7541
Abstract
CVD (cardiovascular disease) has become a significant contributor to premature deaths for many years in Fiji. CVD's late detection also significantly impacts annual deaths and casualties. Currently, Fiji lacks diagnosis tools that would enable people to know their risk levels. In this paper, a machine learning mobile application was developed that can be easily accessible to the local population for early prediction of CVD risk. The design science approach was used to guide the development of the application. The design process involved identifying the problem and motivation, setting objectives, creating a machine-learning mobile application for medical record analysis, demonstrating the application to selected participants, evaluating its usability and the machine-learning model's performance, and communicating the findings. The results revealed that the application proposed in this paper is an effective tool for CVD prediction in Fiji.
Keywords:
Mobile Health, Machine Learning, Design Science, Cardiovascular DiseaseReferences
Alaiad, A., Alsharo, M., & Alnsour, Y. (2019). The determinants of m-health adoption in developing countries: An empirical investigation. Applied Clinical Informatics, 10(05), 820–840. https://doi.org/10.1055/s-0039-1697906
Google Scholar
Armaou, M., Araviaki, E., & Musikanski, L. (2020). eHealth and mHealth interventions for ethnic minority and historically underserved populations in developed countries: An umbrella review. International Journal of Community Well-Being, 3, 193-221. https://doi.org/10.1007/s42413-019-00055-5
Google Scholar
Blattgerste, J., Behrends, J., & Pfeiffer, T. (2022). A web-based analysis toolkit for the system usability scale. 15th International Conference on PErvasive Technologies Related to Assistive Environments (pp. 237-246). Association for Computing Machinery. https://doi.org/10.1145/3529190.3529216
Google Scholar
Curigliano, G., Lenihan, D., Fradley, M., Ganatra, S., Barac, A., Blaes, A., Herrmann, J., Porter, C., Lyon, A. R., Lancellotti, P., Patel, A., DeCara, J., Mitchell, J., Harrison, E., Moslehi, J., Witteles, R., Calabro, M. G., Orecchia, R., De Azambuja, E., … Jordan, K. (2020). Management of cardiac disease in cancer patients throughout oncological treatment: ESMO consensus recommendations. Annals of Oncology, 31(2), 171–190. https://doi.org/10.1016/j.annonc.2019.10.023
Google Scholar
Dasmen, R. N., Fatoni, F., Wijaya, A., Tujni, B., & Nabila, S. (2021). Pelatihan uji kegunaan website menggunakan system usability scale (SUS). ABSYARA: Jurnal Pengabdian Pada Masyarakat, 2(2), 146-158. https://doi.org/10.29408/ab.v2i2.4031
Google Scholar
Del Mar-Raave, J. R., Bahşi, H., Mršić, L., & Hausknecht, K. (2021). A machine learning-based forensic tool for image classification design science approach. Forensic Science International: Digital Investigation, 38, 301265. https://doi.org/10.1016/j.fsidi.2021.301265
Google Scholar
Gumede, D. M., Taylor, M., & Kvalsvig, J. D. (2023). Causes and consequences of critical healthcare skills shortage in the Southern African development community. Development Southern Africa, 40(6), 1174-1199. https://doi.org/10.1080/0376835X.2023.2203155
Google Scholar
Hevner, A., & Gregor, S. (2022). Envisioning entrepreneurship and digital innovation through a design science research lens: A matrix approach. Information & Management, 59(3), 103350. https://doi.org/10.1016/j.im.2020.103350
Google Scholar
Hoque, M. R., Rahman, M. S., Nipa, N. J., & Hasan, M. R. (2020). Mobile health interventions in developing countries: A systematic review. Health Informatics Journal, 26(4), 2792-2810. https://doi.org/10.1177/1460458220937102
Google Scholar
Islam, M. N., Raiyan, K. R., Mitra, S., Mannan, M. R., Tasnim, T., Putul, A. O., & Mandol, A. B. (2023). Predictions: An IoT and machine learning-based system to predict the risk level of cardiovascular diseases. BMC Health Services Research, 23, 171. https://doi.org/10.1186/s12913-023-09104-4
Google Scholar
Kaium, M. A., Bao, Y., Alam, M. Z., & Hoque, M. R. (2020). Understanding continuance usage intention of mHealth in a developing country: An empirical investigation. International Journal of Pharmaceutical and Healthcare Marketing, 14(2), 251-272. https://doi.org/10.1108/IJPHM-06-2019-0041
Google Scholar
Kosarkar, N., Basuri, P., Karamore, P., Gawali, P., Badole, P., & Jumle, P. (2022). Disease prediction using machine learning. 10th International Conference on Emerging Trends in Engineering and Technology-Signal and Information Processing (ICETET-SIP-22) (pp. 1-4). IEEE. https://doi.org/10.1109/ICETET-SIP-2254415.2022.9791739
Google Scholar
Kruse, C., Betancourt, J., Ortiz, S., Valdes Luna, S. M., Bamrah, I. K., & Segovia, N. (2019). Barriers to the use of mobile health in improving health outcomes in developing countries: Systematic review. Journal of Medical Internet Research, 21(10), e13263. https://doi.org/10.2196/13263
Google Scholar
Kumar, B., & Goundar, M. S. (2022). Kid-learn: A mobile language learning application for pre-schoolers. International Journal of Virtual and Personal Learning Environments, 12(1), 1-16. https://doi.org/10.4018/IJVPLE.314950
Google Scholar
Ma, E.-Y., Kim, H., & Lee, U. (2023). Investigating causality in mobile health data through deep learning models. 2023 IEEE International Conference on Big Data and Smart Computing (BigComp) (pp. 375-377). IEEE. https://doi.org/10.1109/BigComp57234.2023.00089
Google Scholar
Ministry of health & medical services. (2015). NCDs in Fiji. https://www.health.gov.fj/ncds/ncds-in fiji/#:~:text=ncds%20in%20fiji&text=in%20recent%20decades%2c%20ncd's%20have,and%20those%20numbers%20are%20growing
Google Scholar
Peffers, K., Tuunanen, T., Rothenberger, M. A., & Chatterjee, S. (2007). A design science research methodology for information systems research. Journal of Management Information Systems, 24(3), 45-77. https://doi.org/10.2753/MIS0742-1222240302
Google Scholar
Poalelungi, D. G., Musat, C. L., Fulga, A., Neagu, M., Neagu, A. I., Piraianu, A. I., & Fulga, I. (2023). Advancing patient care: How artificial intelligence is transforming healthcare. Journal of Personalized Medicine, 13(8), 1214. https://doi.org/10.3390/jpm13081214
Google Scholar
Razzaq, A., Travaglia, J., Raynes-Greenow, C., & Alam, N. A. (2024). Understanding Fijian health system challenges in the prevention of mother-to-child transmission of HIV services in the three tertiary hospitals in Fiji. AIDS Care, 36(7), 954-963. https://doi.org/10.1080/09540121.2024.2331215
Google Scholar
Sharma, S., Lal, R., & Kumar, B. A. (2023). Machine learning for early detection of cardiovascular disease in Fiji. 2023 IEEE Asia-Pacific Conference on Computer Science and Data Engineering (CSDE) (pp. 1-6). IEEE. https://doi.org/10.1109/CSDE59766.2023.10487655
Google Scholar
Sumarsono, S., Sakkinah, I. S., Permanasari, A. E., & Pranggono, B. (2023). Development of a mobile health infrastructure for non-communicable diseases using design science research method: A case study. Journal of Ambient Intelligence and Humanized Computing, 14, 12563-12574. https://doi.org/10.1007/s12652-022-04322-w
Google Scholar
Taylor, R., Lin, S., Linhart, C., & Morrell, S. (2018). Overview of trends in cardiovascular and diabetes risk factors in Fiji. Annals of Human Biology, 45(3), 188-201. 10.1080/03014460.2018.1465122
Google Scholar
Thamilarasan, Y., Ikram, R. R. R., Osman, M., Salahuddin, L., Bujeri, W. Y. W., & Kanchymalay, K. (2023). Enhanced system usability scale using the software quality standard approach. Engineering, Technology & Applied Science Research, 13(5), 11779-11784. https://doi.org/10.48084/etasr.5971
Google Scholar
Tundjungsari, V., Sofro, A. S. M., Yugaswara, H., & Putra, A. T. D. (2018). Development of mobile health application for cardiovascular disease prevention. International Journal of Advanced Computer Science and Applications, 9(11). https://doi.org/10.14569/IJACSA.2018.091175
Google Scholar
Uddin, S., Khan, A., Hossain, M. E., & Moni, M. A. (2019). Comparing different supervised machine learning algorithms for disease prediction. BMC Medical Informatics and Decision Making, 19, 281. https://doi.org/10.1186/s12911-019-1004-8
Google Scholar
Wen, Z., & Huang, H. (2022). The potential for artificial intelligence in healthcare. Journal of Commercial Biotechnology, 27(4). https://doi.org/10.5912/jcb1327
Google Scholar
Zulzalil, H., Rahmat, H., Abd Ghani, A. A., & Kamaruddin, A. (2023). Expert review on usefulness of an integrated checklist-based mobile usability evaluation framework. Journal of Computer Science Research, 5(3), 57-73. https://doi.org/10.30564/jcsr.v5i3.5816
Google Scholar
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