Design and Development of Diabetes Management System Using Machine Learning

被引:24
|
作者
Sowah, Robert A. [1 ]
Bampoe-Addo, Adelaide A. [1 ]
Armoo, Stephen K. [1 ]
Saalia, Firibu K. [2 ,3 ]
Gatsi, Francis [4 ]
Sarkodie-Mensah, Baffour [1 ]
机构
[1] Univ Ghana, Dept Comp Engn, POB LG 77, Legon, Accra, Ghana
[2] Univ Ghana, Dept Food Proc Engn, POB LG 77, Legon, Accra, Ghana
[3] Univ Ghana, Dept Nutr & Food Sci, POB LG 77, Legon, Accra, Ghana
[4] Ashesi Univ, Dept Engn & Comp Sci, Berekuso, Eastern Region, Ghana
关键词
SUPPORT VECTOR MACHINES; DIAGNOSIS; EQUATION;
D O I
10.1155/2020/8870141
中图分类号
R19 [保健组织与事业(卫生事业管理)];
学科分类号
摘要
This paper describes the design and implementation of a software system to improve the management of diabetes using a machine learning approach and to demonstrate and evaluate its effectiveness in controlling diabetes. The proposed approach for this management system handles the various factors that affect the health of people with diabetes by combining multiple artificial intelligence algorithms. The proposed framework factors the diabetes management problem into subgoals: building a Tensorflow neural network model for food classification; thus, it allows users to upload an image to determine if a meal is recommended for consumption; implementing K-Nearest Neighbour (KNN) algorithm to recommend meals; using cognitive sciences to build a diabetes question and answer chatbot; tracking user activity, user geolocation, and generating pdfs of logged blood sugar readings. The food recognition model was evaluated with cross-entropy metrics that support validation using Neural networks with a backpropagation algorithm. The model learned features of the images fed from local Ghanaian dishes with specific nutritional value and essence in managing diabetics and provided accurate image classification with given labels and corresponding accuracy. The model achieved specified goals by predicting with high accuracy, labels of new images. The food recognition and classification model achieved over 95% accuracy levels for specific calorie intakes. The performance of the meal recommender model and question and answer chatbot was tested with a designed cross-platform user-friendly interface using Cordova and Ionic Frameworks for software development for both mobile and web applications. The system recommended meals to meet the calorific needs of users successfully using KNN (withk=5) and answered questions asked in a human-like way. The implemented system would solve the problem of managing activity, dieting recommendations, and medication notification of diabetics.
引用
收藏
页数:17
相关论文
共 50 条
  • [41] Design and Development of an Efficient Demographic-based Movie Recommender System using Hybrid Machine Learning Techniques
    Paranjape, Vishal
    Nihalani, Neelu
    Mishra, Nishchol
    [J]. INTERNATIONAL JOURNAL OF COMPUTERS COMMUNICATIONS & CONTROL, 2024, 19 (04)
  • [42] Design and Development of an Efficient Network Intrusion Detection System using Ensemble Machine Learning Techniques for Wifi Environments
    Das, Abhijit
    Pramod
    [J]. INTERNATIONAL JOURNAL OF ADVANCED COMPUTER SCIENCE AND APPLICATIONS, 2022, 13 (04) : 856 - 866
  • [43] Automating spoken dialogue management design using machine learning: An industry perspective
    Paek, Tim
    Pieraccini, Roberto
    [J]. SPEECH COMMUNICATION, 2008, 50 (8-9) : 716 - 729
  • [44] Big data and machine learning to tackle diabetes management
    Pina, Ana F.
    Meneses, Maria Joao
    Sousa-Lima, Ines
    Henriques, Roberto
    Raposo, Joao F.
    Macedo, Maria Paula
    [J]. EUROPEAN JOURNAL OF CLINICAL INVESTIGATION, 2023, 53 (01)
  • [45] Development of Flexible Autonomous Car System Using Machine Learning and Blockchain
    Ramachandran, S. Shreyas
    Veeraraghavan, A. K.
    Karni, Uvais
    Sivaraman, K.
    [J]. PROCEEDINGS OF THE 3RD INTERNATIONAL SYMPOSIUM OF INFORMATION AND INTERNET TECHNOLOGY (SYMINTECH 2018), 2019, 565 : 63 - 72
  • [46] A SYSTEM TO DETECT MENTAL STRESS USING MACHINE LEARNING AND MOBILE DEVELOPMENT
    Vuppalapati, Chandrasekar
    Khan, Mohamad S.
    Raghu, Nisha
    Veluru, Priyanka
    Khursheed, Suma
    [J]. PROCEEDINGS OF 2018 INTERNATIONAL CONFERENCE ON MACHINE LEARNING AND CYBERNETICS (ICMLC), VOL 1, 2018, : 161 - 166
  • [47] Development of a smart multiphase system for disperse flows using machine learning
    Broumand, Mohsen
    Yun, Sean
    Hong, Zekai
    [J]. INTERNATIONAL JOURNAL OF MULTIPHASE FLOW, 2024, 174
  • [48] Development of a Dynamic Diagnosis Grading System for Infertility Using Machine Learning
    Liao, ShuJie
    Pan, Wei
    Dai, Wan-qiang
    Jin, Lei
    Huang, Ge
    Wang, Renjie
    Hu, Cheng
    Pan, Wulin
    Tu, Haiting
    [J]. JAMA NETWORK OPEN, 2020, 3 (11) : E2023654
  • [49] Development of Instructional Design ICARE Assisted Learning Management System to Enhance the Learning Process
    Utami, Wikan Budi
    Aulia, Fikri
    Budiman, M. Arif S.
    [J]. PROCEEDINGS OF THE 3RD INTERNATIONAL CONFERENCE ON EDUCATION AND TRAINING (ICET 2017), 2017, 128 : 34 - 38
  • [50] Enhancing E-commerce Management with Machine Learning and Internet of Things: Design and Development
    Pang, Dikai
    Wang, Shuodong
    Ge, Dong
    Lin, Wei
    Kang, Yaqi
    Li, Rongtingyu
    [J]. JOURNAL OF THE KNOWLEDGE ECONOMY, 2024,