Automatic user sentiments extraction from diabetes mobile apps – An evaluation of reviews with machine learning

References

• Saeedi P, Petersohn I, Salpea P, Malanda B, Karuranga S, Unwin N, Colagiuri S, Guariguata L, Motala AA, Ogurtsova K, et al. Global and regional diabetes prevalence estimates for 2019 and projections for 2030 and 2045: results from the International Diabetes Federation Diabetes Atlas. Diabetes Research and Clinical Practice. 2019 Nov 1;157:107843. doi:10.1016/j.diabres.2019.107843.
   PubMed Web of Science ®Google Scholar
• Angelini S, Alicastro GM, Dionisi S, Di Muzio M. Structure and characteristics of diabetes self-management applications: a systematic review of the literature. CIN: Computers, Informatics, Nursing. 2019 Jul 1;37(7):340–48.
   PubMed Web of Science ®Google Scholar
• Adu MD, Malabu UH, Malau-Aduli AE, Malau-Aduli BS. The development of My Care Hub mobile-phone app to support self-management in Australians with type 1 or type 2 diabetes. Scientific Reports. 2020 Jan 8;10(1):1–0. doi:10.1038/s41598-019-56411-0
   PubMed Web of Science ®Google Scholar
• Zhang Y, Li X, Luo S, Liu C, Xie Y, Guo J, Liu F, Zhou Z. Use, perspectives, and attitudes regarding diabetes management mobile apps among diabetes patients and diabetologists in China: national web-based survey. JMIR mHealth and uHealth. 2019 Feb 8;7(2):e12658. doi:10.2196/12658
   PubMed Web of Science ®Google Scholar
• Fu H, McMahon SK, Gross CR, Adam TJ, Wyman JF. Usability and clinical efficacy of diabetes mobile applications for adults with type 2 diabetes: a systematic review. Diabetes Research and Clinical Practice. 2017 Sep 1;131:70–81. doi:10.1016/j.diabres.2017.06.016.
   PubMed Web of Science ®Google Scholar
• Ellahham S. Artificial intelligence in diabetes care. Am J Med. 2020;133(8):895–900. doi:10.1016/j.amjmed.2020.03.033.
   PubMed Web of Science ®Google Scholar
• Woldaregay AZ, Årsand E, Botsis T, Albers D, Mamykina L, Hartvigsen G. Data-driven blood glucose pattern classification and anomalies detection: machine-learning applications in type 1 diabetes. Journal of Medical Internet Research. 2019 May 1;21(5):e11030. doi:10.2196/11030
   PubMed Web of Science ®Google Scholar
• Xie LJ, Cheng MH. Body adipose distribution among patients with type 2 diabetes mellitus. Obesity Research & Clinical Practice. 2012 Oct 1;6(4):e270–9. doi:10.1016/j.orcp.2012.09.003
   Web of Science ®Google Scholar
• Kahn SE. The relative contributions of insulin resistance and beta-cell dysfunction to the pathophysiology of type 2 diabetes. Diabetologia. 2003 Jan;46(1):3–19. doi:10.1007/s00125-002-1009-0.
   PubMed Web of Science ®Google Scholar
• Ali R, Siddiqi MH, Idris M, Kang BH, Lee S. Prediction of diabetes mellitus based on boosting ensemble modeling. InInternational conference on ubiquitous computing and ambient intelligence 2014 Dec 2 (pp. 25–28). Springer, Cham.
   Google Scholar
• Yuvaraj N, SriPreethaa KR. Diabetes prediction in healthcare systems using machine learning algorithms on Hadoop cluster. Cluster Computing. 2019 Jan;22(1):1–9. doi:10.1007/s10586-017-1532-x.
   Google Scholar
• Oyebode O, Alqahtani F, Orji R. Using machine learning and thematic analysis methods to evaluate mental health apps based on user reviews. IEEE Access. 2020 Jun 12;8:111141–58. doi:10.1109/ACCESS.2020.3002176.
   Web of Science ®Google Scholar
• Salas-Zárate MD, Medina-Moreira J, Lagos-Ortiz K, Luna-Aveiga H, Rodriguez-Garcia MA, Valencia-Garcia R. Sentiment analysis on tweets about diabetes: an aspect-level approach. Computational and Mathematical Methods in Medicine. 2017 Feb 19;2017:1–9. doi:10.1155/2017/5140631.
   Web of Science ®Google Scholar
• Ramsingh J, Bhuvaneswari V. An efficient map reduce-based hybrid NBC-TFIDF algorithm to mine the public sentiment on diabetes mellitus–a big data approach. Journal of King Saud University-Computer and Information Sciences. 2021 Oct 1;33(8):1018–29. doi:10.1016/j.jksuci.2018.06.011.
   Web of Science ®Google Scholar
• Reichert JR, Kristensen KL, Mukkamala RR, Vatrapu R. A supervised machine learning study of online discussion forums about type-2 diabetes. In2017 IEEE 19Th International Conference on E-health Networking, Applications and Services (Healthcom) 2017 Oct 12 (pp. 1–7). IEEE.
   Google Scholar
• Oyebode O, Orji R, 2019, October. Detecting factors responsible for diabetes prevalence in Nigeria using social media and machine learning. In 2019 15th International Conference on Network and Service Management (CNSM) (pp. 1–4). IEEE.
   Google Scholar
• Miller RG, Secrest AM, Sharma RK, Songer TJ, Orchard TJ. Improvements in the life expectancy of type 1 diabetes: the Pittsburgh epidemiology of diabetes complications study cohort. Diabetes. 2012;61(11):2987–92. doi:10.2337/db11-1625.
   PubMed Web of Science ®Google Scholar
• Litchman ML, Snider C, Edelman LS, Wawrzynski SE, Gee PM. Diabetes online community user perceptions of successful aging with diabetes: analysis of a# DSMA tweet chat. JMIR Aging. 2018 Jun 22;1(1):e10176. doi:10.2196/10176
   PubMedGoogle Scholar
• Gabarron E, Dorronzoro E, Rivera-Romero O, Wynn R. Diabetes on Twitter: a sentiment analysis. J Diabetes Sci Technol. 2019;13(3):439–44. doi:10.1177/1932296818811679.
   PubMedGoogle Scholar
• Sivanandam SN, Paulraj M, Nithyanandam M .2000 Mar 30. Certain improvements in back propagation procedure for pattern identification. In Applications and science of computational intelligence III. SPIE. Vol. 4055. p. 79–89.
   Google Scholar
• Smola A, Vishwanathan SV. Introduction to machine learning. Cambridge University, UK. 2008;32:2008.
   Google Scholar
• International Diabetes Federation (IDF). 2017. Self-monitoring of blood glucose in non-insulin treated type 2 diabetes available from . Accessed 2020 August 06; https://www.idf.org/e-library/guidelines/85-self-430monitoring-of-blood-glucose-in-non-insulin-treated-type-2-diabetes.html&#x003E
   Google Scholar
• Powers MA, Bardsley J, Cypress M, Duker P, Funnell MM, Hess Fischl A, Maryniuk MD, Siminerio L, Vivian E. Diabetes self-management education and support in type 2 diabetes: a joint position statement of the American Diabetes Association, the American Association of Diabetes Educators, and the Academy of Nutrition and Dietetics. Diabetes Care. 2015 Jul 1;38(7):1372–82. doi:10.2337/dc15-0730.
   PubMed Web of Science ®Google Scholar
• Lavelle D, Zeitoun J, Stern M, Butkiewicz E, Wegner E, Reinisch C. Diabetes self-management education in the home. Cureus. 2016 Jul 25;8(7). doi:10.7759/cureus.710.
   PubMedGoogle Scholar
• Lee DD, Seung HS. Learning the parts of objects by non-negative matrix factorization. Nature. 1999;401(6755):788–91. doi:10.1038/44565.
   PubMed Web of Science ®Google Scholar
• Gillis N. The why and how of nonnegative matrix factorization. Regularization, Optim, Kernels, and Support Vector Mach. 2014;12:257–91.
   Google Scholar
• Buitinck L, Louppe G, Blondel M, Pedregosa F, Mueller A, Grisel O, Niculae V, Prettenhofer P, Gramfort A, Grobler J, et al. API design for machine learning software: experiences from the scikit-learn project. arXiv preprint arXiv:1309.0238. 2013 Sep 1.
   Google Scholar
• Pedregosa F, Varoquaux G, Gramfort A, Michel V, Thirion B, Grisel O, Blondel M, Prettenhofer P, Weiss R, Dubourg V, et al. Scikit-learn: machine learning in Python. The Journal of Machine Learning Research. 2011 Nov 1;12:2825–30.
   Web of Science ®Google Scholar
• Rana A, Rawat AS, Bijalwan A, Bahuguna H. Application of multi layer (Perceptron) artificial neural network in the diagnosis system: a systematic review. 2018 International conference on research in intelligent and computing in engineering (RICE); 2018, August; (pp. 1–6). IEEE
   Google Scholar
• Kingma DP, Ba J. Adam: a method for stochastic optimization. arXiv preprint arXiv. 2014;1412:6980.
   Google Scholar
• Amari SI. Backpropagation and stochastic gradient descent method. Neurocomputing. 1993;5(4–5):185–96. doi:10.1016/0925-2312(93)90006-O.
   Web of Science ®Google Scholar
• Panchal G, Ganatra A, Kosta YP, Panchal D. Behaviour analysis of multilayer perceptrons with multiple hidden neurons and hidden layers. International Journal of Computer Theory and Engineering. 2011 Apr;3(2):332–37. doi:10.7763/IJCTE.2011.V3.328.
   Google Scholar
• Veazie S, Winchell K, Gilbert J, Paynter R, Ivlev I, Eden KB, Nussbaum K, Weiskopf N, Guise JM, Helfand M. Rapid evidence review of mobile applications for self-management of diabetes. Journal of General Internal Medicine. 2018 Jul;33(7):1167–76. doi:10.1007/s11606-018-4410-1.
   PubMed Web of Science ®Google Scholar
• Ye Q, Khan U, Boren SA, Simoes EJ, Kim MS. An analysis of diabetes mobile applications features compared to AADE7™: addressing self-management behaviors in people with diabetes. Journal of Diabetes Science and Technology. 2018 Jul;12(4):808–16. doi:10.1177/1932296818754907.
   PubMedGoogle Scholar
• Adu MD, Malabu UH, Malau-Aduli AE, Malau-Aduli BS, Borsci S. Users’ preferences and design recommendations to promote engagements with mobile apps for diabetes self-management: multi-national perspectives. PLoS One. 2018 Dec 10;13(12):e0208942. doi:10.1371/journal.pone.0208942.
   PubMed Web of Science ®Google Scholar
• Brzan PP, Rotman E, Pajnkihar M, Klanjsek P. Mobile applications for control and self management of diabetes: a systematic review. Journal of Medical Systems. 2016 Sep;40(9):1–0. doi:10.1007/s10916-016-0564-8.
   PubMed Web of Science ®Google Scholar
• Goyal S, Cafazzo JA. Mobile phone health apps for diabetes management: current evidence and future developments. QJM: An International Journal of Medicine. 2013 Dec 1;106(12):1067–69. doi:10.1093/qjmed/hct203.
   PubMed Web of Science ®Google Scholar
• Wang X, He L, Zhu K, Zhang S, Xin L, Xu W, Guan Y. An integrated model to evaluate the impact of social support on improving self-management of type 2 diabetes mellitus. BMC Medical Informatics and Decision Making. 2019 Dec;19(1):1–2. doi:10.1186/s12911-018-0723-6.
   PubMed Web of Science ®Google Scholar
• 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. Diabetology & Metabolic Syndrome. 2019 Dec;11(1):1–7. doi:10.1186/s13098-019-0480-4.
   PubMed Web of Science ®Google Scholar
• Fleming GA, Petrie JR, Bergenstal RM, Holl RW, Peters AL, Heinemann L. Diabetes digital app technology: benefits, challenges, and recommendations. A consensus report by the European Association for the Study of Diabetes (EASD) and the American Diabetes Association (ADA) Diabetes Technology Working Group. Diabetes Care. 2020 Jan 1;43(1):250–60. doi:10.2337/dci19-0062.
   PubMed Web of Science ®Google Scholar
• Medina-Moreira J, García-Díaz JA, Apolinardo-Arzube O, Luna-Aveiga H, Valencia-García R. Mining twitter for measuring social perception towards diabetes and obesity in Central America. InInternational Conference on Technologies and Innovation 2019 Dec 2 (pp. 81–94). Springer, Cham.
   Google Scholar