Abstract:
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.
