Sefa-Yeboah Sylvester M, Osei Annor Kwabena, Koomson Valencia J, Saalia Firibu K, Steiner-Asiedu Matilda, Mills Godfrey A
Department of Computer Engineering, University of Ghana, P.O. Box LG, 77 Legon, Ghana.
Department of Electrical and Computer Engineering, Tufts University, Medford, MA 02155, USA.
Int J Telemed Appl. 2021 Aug 27;2021:6624057. doi: 10.1155/2021/6624057. eCollection 2021.
Obesity is a major global health challenge and a risk factor for the leading causes of death, including heart disease, stroke, diabetes, and several types of cancer. Attempts to manage and regulate obesity have led to the implementation of various dietary regulatory initiatives to provide information on the calorie contents of meals. Although knowledge of the calorie content is useful for meal planning, it is not sufficient as other factors, including health status (diabetes, hypertension, etc.) and level of physical activity, are essential in the decision process for obesity management. In this work, we present an artificial intelligence- (AI-) based application that is driven by a genetic algorithm (GA) as a potential tool for tracking a user's energy balance and predicting possible calorie intake required to meet daily calorie needs for obesity management. The algorithm takes the users' input information on desired foods which are selected from a database and extracted records of users on cholesterol level, diabetes status, and level of physical activity, to predict possible meals required to meet the users need. The micro- and macronutrients of food content are used for the computation and prediction of the potential foods required to meet the daily calorie needs. The functionality and performance of the model were tested using a sample of 30 volunteers from the University of Ghana. Results revealed that the model was able to predict both glycemic and non-glycemic foods based on the condition of the user as well as the macro- and micronutrients requirements. Moreover, the system is able to adequately track the progress of the user's weight loss over time, daily nutritional needs, daily calorie intake, and predictions of meals that must be taken to avoid compromising their health. The proposed system can serve as a useful resource for individuals, dieticians, and other health management personnel for managing obesity, patients, and for training students in fields of dietetics and consumer science.
肥胖是一项重大的全球健康挑战,也是包括心脏病、中风、糖尿病和几种癌症在内的主要死因的风险因素。为管理和控制肥胖所做的努力导致实施了各种饮食监管举措,以提供有关膳食卡路里含量的信息。尽管了解卡路里含量对膳食计划有用,但这还不够,因为其他因素,包括健康状况(糖尿病、高血压等)和身体活动水平,在肥胖管理的决策过程中至关重要。在这项工作中,我们展示了一种基于人工智能(AI)的应用程序,该程序由遗传算法(GA)驱动,作为一种潜在工具,用于跟踪用户的能量平衡,并预测为实现肥胖管理的每日卡路里需求所需的可能卡路里摄入量。该算法获取用户从数据库中选择的所需食物的输入信息,以及用户关于胆固醇水平、糖尿病状况和身体活动水平的记录,以预测满足用户需求所需的可能膳食。食物成分的微量和宏量营养素用于计算和预测满足每日卡路里需求所需的潜在食物。使用来自加纳大学的30名志愿者样本对该模型的功能和性能进行了测试。结果表明,该模型能够根据用户的状况以及宏量和微量营养素需求预测血糖和非血糖食物。此外,该系统能够充分跟踪用户体重减轻的进展、每日营养需求、每日卡路里摄入量以及为避免损害健康而必须食用的膳食预测。所提出的系统可以作为个人、营养师和其他健康管理人员管理肥胖患者的有用资源,也可用于营养学和消费者科学领域的学生培训。