MachineLearning based Analysis of Mobile Apps for People with Alzheimer's Disease

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

Citation 

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.

Abstract
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.

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