Design and Development of Diabetes Management System Using Machine Learning

Robert A Sowah; Adelaide A Bampoe-Addo; Stephen K Armoo; Firibu K Saalia; Francis Gatsi; Baffour Sarkodie-Mensah

doi:10.1155/2020/8870141

Design and Development of Diabetes Management System Using Machine Learning

Int J Telemed Appl. 2020 Jul 16:2020:8870141. doi: 10.1155/2020/8870141. eCollection 2020.

Authors

Robert A Sowah¹, Adelaide A Bampoe-Addo¹, Stephen K Armoo¹, Firibu K Saalia², Francis Gatsi³, Baffour Sarkodie-Mensah¹

Affiliations

¹ Department of Computer Engineering, University of Ghana, P.O. Box LG 77, Legon, Accra-, Ghana.
² Department of Food Process Engineering, And Department of Nutrition and Food Science, University of Ghana, P.O. Box LG 77, Legon, Accra-, Ghana.
³ Department of Engineering and Computer Science, Ashesi University, Berekuso, Eastern Region, Ghana.

Abstract

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.