利用机器学习设计和开发糖尿病管理系统。

IF 3.1 Q2 HEALTH CARE SCIENCES & SERVICES International Journal of Telemedicine and Applications Pub Date : 2020-07-16 eCollection Date: 2020-01-01 DOI:10.1155/2020/8870141
Robert A Sowah, Adelaide A Bampoe-Addo, Stephen K Armoo, Firibu K Saalia, Francis Gatsi, Baffour Sarkodie-Mensah
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引用次数: 0

摘要

本文介绍了一个软件系统的设计和实施,该系统利用机器学习方法改善糖尿病的管理,并演示和评估其在控制糖尿病方面的有效性。该管理系统的拟议方法通过结合多种人工智能算法来处理影响糖尿病患者健康的各种因素。所提出的框架将糖尿病管理问题分为几个子目标:建立一个用于食物分类的 Tensorflow 神经网络模型;因此,它允许用户上传图片,以确定是否推荐食用某一餐;实施 K-Nearest Neighbour (KNN) 算法来推荐膳食;利用认知科学来建立一个糖尿病问答聊天机器人;跟踪用户活动、用户地理位置,并生成记录的血糖读数的 pdf 文件。通过交叉熵指标对食物识别模型进行了评估,该指标支持采用反向传播算法的神经网络进行验证。该模型学习了从加纳本地菜肴中提取的图像特征,这些特征具有特定的营养价值和管理糖尿病患者的精髓,并根据给定的标签和相应的准确率提供了准确的图像分类。该模型通过高精度预测新图像的标签实现了指定目标。食物识别和分类模型对特定卡路里摄入量的准确率超过 95%。膳食推荐模型和问答聊天机器人的性能通过使用 Cordova 和 Ionic 框架设计的跨平台用户友好界面进行了测试,该界面用于移动和网络应用程序的软件开发。该系统利用 KNN(k = 5)成功推荐了满足用户热量需求的膳食,并以类似人类的方式回答了用户提出的问题。该系统将解决糖尿病患者的活动管理、节食建议和用药通知问题。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

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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 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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来源期刊
CiteScore
6.90
自引率
2.30%
发文量
19
审稿时长
12 weeks
期刊介绍: The overall aim of the International Journal of Telemedicine and Applications is to bring together science and applications of medical practice and medical care at a distance as well as their supporting technologies such as, computing, communications, and networking technologies with emphasis on telemedicine techniques and telemedicine applications. It is directed at practicing engineers, academic researchers, as well as doctors, nurses, etc. Telemedicine is an information technology that enables doctors to perform medical consultations, diagnoses, and treatments, as well as medical education, away from patients. For example, doctors can remotely examine patients via remote viewing monitors and sound devices, and/or sampling physiological data using telecommunication. Telemedicine technology is applied to areas of emergency healthcare, videoconsulting, telecardiology, telepathology, teledermatology, teleophthalmology, teleoncology, telepsychiatry, teledentistry, etc. International Journal of Telemedicine and Applications will highlight the continued growth and new challenges in telemedicine, applications, and their supporting technologies, for both application development and basic research. Papers should emphasize original results or case studies relating to the theory and/or applications of telemedicine. Tutorial papers, especially those emphasizing multidisciplinary views of telemedicine, are also welcome. International Journal of Telemedicine and Applications employs a paperless, electronic submission and evaluation system to promote a rapid turnaround in the peer-review process.
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