Machine-Learning based Analysis of Mobile Apps for People with Alzheimer's Disease

  IJETT-book-cover  International Journal of Engineering Trends and Technology (IJETT)          
© 2021 by IJETT Journal
Volume-69 Issue-2
Year of Publication : 2021
Authors : Roobaea Alroobaea, Mariem Haoues, Saeed Rubaiee, Anas Ahmed, Fahad Almansour
DOI :  10.14445/22315381/IJETT-V69I2P218


MLA Style: Roobaea Alroobaea, Mariem Haoues, Saeed Rubaiee, Anas Ahmed, Fahad Almansour  "Machine-Learning based Analysis of Mobile Apps for People with Alzheimer's Disease" International Journal of Engineering Trends and Technology 69.2(2021):126-133. 

APA Style:Roobaea Alroobaea, Mariem Haoues, Saeed Rubaiee, Anas Ahmed, Fahad Almansour. Machine-Learning based Analysis of Mobile Apps for People with Alzheimer's Disease. International Journal of Engineering Trends and Technology, 69(2), 126-133.

Although Alzheimer's is a progressive disease, individuals with Alzheimer's can have a normal life if they manage their lifestyle correctly and control the disease's symptoms. Currently, mobile apps provide a helpful solution for disease management assistance outside hospitals. Reviewing mobile apps users’ feedback allows developers to better understand patients’ needs and guarantee their satisfaction. In this paper, we analyze user reviews suggested on 10 selected mobile apps for individuals with Alzheimer's. A total of 1675 user reviews have been collected, including positive and negative opinions. This analysis has been performed based on machine learning and natural language processing techniques. The best performance was provided by the support vector machine classifier with accuracy equal to 99.43% in classifying user reviews into positive and negative reviews. The results of this analysis showed that users are not satisfied with the quality of the mobile apps available for people with Alzheimer's, especially the usability.

[1] A. Turner, How many smartphones are in the world? 2020.
[2] Mobius MD, The medical workflow company, 11 surprising mobile health statistics, 2019.
[3] C. Patterson, The state of the art of dementia research: New frontiers, World Alzheimer Report, 2018.
[4] World Health Organization, Alzheimer’s disease fact sheet, 2020.
[5] G. Gupta, A. Gupta, V. Jaiswal, and M. D. Ansari, A review and analysis of mobile health applications for Alzheimer's patients and caregivers, in 2018 Fifth International Conference on Parallel, Distributed and Grid Computing (PDGC). IEEE, 2018, 171–175.
[6] D. O. Hebb, The organization of behavior: a neuropsychological theory. J. Wiley; Chapman & Hall, 1949.
[7] E. Alpaydin, Introduction to machine learning. MIT Press, 2020.
[8] U. Kumar, Applications of machine learning in disease pre-screening, in Pre-Screening Systems for Early Disease Prediction, Detection, and Prevention. IGI Global, 2019, 278–320.
[9] T. Alanzi, A review of mobile applications available in the app and google play stores used during the covid-19 outbreak,” Journal of Multidisciplinary Healthcare, 14, 45–57.
[10] E. Alickovic and A. Subasi, Automatic detection of Alzheimer's disease based on histogram and random forest, in CMBEBIH 2019, A. Badnjevic, R. Skrbi ˇ c, and L. Gurbeta Pokvi ´ c, Eds. Springer International ´ Publishing, 2020.
[11] Alzheimer’s Association, Alzheimer’s disease facts and figures, Alzheimer’s & Dementia, 15(3)(2019) 321–387.
[12] C. Yamagata, J. F. Coppola, M. Kowtko, and S. Joyce, Mobile app development and usability research to help dementia and Alzheimer's patients, in 2013 IEEE Long Island Systems, Applications, and Technology Conference (LISAT), 2013, 1–6.
[13] N. Aljojo, R. Alotaibi, B. Alharbi, A. Alshutayri, A. T. Jamal, A. Banjar, M. Khayyat, A. Zainol, A. Al-Roqy, A.-M. Rahaf, et al., Alzheimer assistant: a mobile application using machine learning, Romanian Journal of Information Technology and Automatic Control, 30(4)(2020) 7–26.
[14] O. Oyebode, F. Alqahtani, and R. Orji, Using machine learning and thematic analysis methods to evaluate mental health apps based on user reviews, 8 IEEE, 2020, 111 141–111 158.
[15] N. Benalaya, M. Haoues, and A. Sellami, Diabetes self-management mobile apps improvement based on classifying user reviews, in International Conference on Intelligent Systems Designs and Applications (in the process), 2020.
[16] J. Nicholas, A. S. Fogarty, K. Boydell, and H. Christensen, The reviews are in: a qualitative content analysis of consumer perspectives on apps for bipolar disorder, Journal of medical Internet research, 19(4), p. e105, 2017.
[17] F. Luna-Perejon, S. Malwade, C. Styliadis, J. Civit, D. CascadoCaballero, E. Konstantinidis, S. S. Abdul, P. D. Bamidis, A. Civit, and Y.-C. J. Li, Evaluation of user satisfaction and usability of a mobile app for smoking cessation, Computer methods and programs in biomedicine, 182, 2019.
[18] S. K. Choi, B. Yelton, V. K. Ezeanya, K. Kannaley, and D. B. Friedman, Review of the content and quality of mobile applications about Alzheimer's disease and related dementias, Journal of Applied Gerontology, 39(6)(202) 601–608.
[19] Y. Guo, F. Yang, F. Hu, W. Li, N. Ruggiano, and H. Y. Lee, Existing mobile phone apps for self-care management of people with Alzheimer's disease and related dementias: Systematic analysis, JMIR aging, 3(1)(2020).
[20] Heedzy, Download app reviews from iTunes app store amp; google play. Accessed: Jan. 01, 2021. available:, 2021.
[21] C. P. Medina and M. R. R. Ramon, Using tf-idf to determine word relevance in document query juan, New Educational Review, 42(4)(2015) 40–51.
[22] Alroobaea, R., 2020. An Empirical combination of Machine Learning models to Enhance author profiling performance. International Journal of advanced trends in Computer science and engineering 9(2)(2020) 2130 -2137.
[23] Roobaea Alroobaea, Sali Alafif, Shomookh Alhomidi, Ahad Aldahass, Reem Hamed, Rehab Mulla, and Bedour Alotaibi, A Decision Support System for Detecting Age and Gender from Twitter Feeds based on a Comparative Experiments, International Journal of Advanced Computer Science and Applications (IJACSA), 11(12), 2020.
[24] P. Seetha Subha Priya, S. Nandhinidevi, Dr. M. Thangamani, Dr. S. Nallusamy, A Review on Exploring the Deep Learning Concepts and Applications for Medical Diagnosis, International Journal of Engineering Trends and Technology 68(10) (2020) 63-66.

Alzheimer's disease; Machine learning; Natural Language Processing; Opinion Analysis; Mobile apps.