Mobile Application for Calorie Control Using Machine Learning

• Autores: Huamani-Tito, Kelly Rocio, Huaman-La Cruz, Gerardo Francisco, Herrera-Trujillo, Emilio Antonio
• Formato: artículo
• Fecha de Publicación: 2024
• Institución: Universidad Peruana de Ciencias Aplicadas
• Repositorio: UPC-Institucional
• Lenguaje: inglés
• OAI Identifier: oai:repositorioacademico.upc.edu.pe:10757/676093
• Enlace del recurso: http://hdl.handle.net/10757/676093
• Nivel de acceso: acceso embargado
• Materia: Excess weight; Food recommendation; Mobile solution; Support vector machine

Overweight is a serious problem in Peru, and compliance with a healthy and balanced diet is essential for its management. Despite the existence of different types of diets and meal plans, people still find it difficult to comply with the treatment. Also, lack of access to adequate nutritional information and lack of follow-up in implementing a low-calorie diet can demotivate people and reduce the effectiveness of the process. To reduce this problem, a mobile application is presented that allows the control of the caloric intake of food, based on food suggestions using Machine Learning. For this purpose, the Support Vector Machine algorithm is applied to train a model that recommends personalized food to users according to their preferences. This is how the application allows users to easily access personalized food recommendations by analyzing their preferences and caloric needs individually. As a result, it has achieved a Percentage of Caloric Recommendation Satisfaction of 90.5%, a Percentage of Accepted Recommendations of 94.2%, and a Percentage of Usage Satisfaction of 92.6%. These results support its effectiveness and ease of use. It is expected that this proposal will have a positive impact on the fight against overweight in Peru.