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Design and Development of Diabetes Management System Using Machine Learning

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...

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Autores principales: Sowah, Robert A., Bampoe-Addo, Adelaide A., Armoo, Stephen K., Saalia, Firibu K., Gatsi, Francis, Sarkodie-Mensah, Baffour
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Hindawi 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7381989/
https://www.ncbi.nlm.nih.gov/pubmed/32724304
http://dx.doi.org/10.1155/2020/8870141
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author Sowah, Robert A.
Bampoe-Addo, Adelaide A.
Armoo, Stephen K.
Saalia, Firibu K.
Gatsi, Francis
Sarkodie-Mensah, Baffour
author_facet Sowah, Robert A.
Bampoe-Addo, Adelaide A.
Armoo, Stephen K.
Saalia, Firibu K.
Gatsi, Francis
Sarkodie-Mensah, Baffour
author_sort Sowah, Robert A.
collection PubMed
description 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 (with k = 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.
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spelling pubmed-73819892020-07-27 Design and Development of Diabetes Management System Using Machine Learning Sowah, Robert A. Bampoe-Addo, Adelaide A. Armoo, Stephen K. Saalia, Firibu K. Gatsi, Francis Sarkodie-Mensah, Baffour Int J Telemed Appl Research Article 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 (with k = 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. Hindawi 2020-07-16 /pmc/articles/PMC7381989/ /pubmed/32724304 http://dx.doi.org/10.1155/2020/8870141 Text en Copyright © 2020 Robert A. Sowah et al. http://creativecommons.org/licenses/by/4.0/ This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
spellingShingle Research Article
Sowah, Robert A.
Bampoe-Addo, Adelaide A.
Armoo, Stephen K.
Saalia, Firibu K.
Gatsi, Francis
Sarkodie-Mensah, Baffour
Design and Development of Diabetes Management System Using Machine Learning
title Design and Development of Diabetes Management System Using Machine Learning
title_full Design and Development of Diabetes Management System Using Machine Learning
title_fullStr Design and Development of Diabetes Management System Using Machine Learning
title_full_unstemmed Design and Development of Diabetes Management System Using Machine Learning
title_short Design and Development of Diabetes Management System Using Machine Learning
title_sort design and development of diabetes management system using machine learning
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7381989/
https://www.ncbi.nlm.nih.gov/pubmed/32724304
http://dx.doi.org/10.1155/2020/8870141
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