Food recommender system for Diabetes Type 2 patients

Abstract

Diabetes mellitus is a chronic medical disorder that arises when the body does not produce enough insulin or when the body does not utilize the insulin produced effectively. Insulin is the hormone that regulates blood sugar in our bodies, when blood sugar is not regulated it leads to hyperglycemia which is excess blood sugar levels or hypoglycemia which is very low blood sugar levels. Due to urbanization that comes with very busy day to day schedules patients tend to pay little or no attention to their eating patterns leading them to opt for quick fixes such as fast foods, less balanced diets, and intake of a lot of processed food. The number of cases of diabetes patients has been steadily incrementing over the past few decades. Lack of proper diabetes management results in long-standing complications that end up using up on an individual’s resources such as money and time spent seeking medical attention now and then. Diabetes management and control of blood sugar levels are usually done through pharmacotherapy which is the use of medication alongside nutritional therapy which involves eating healthy diets. For nutrition therapy to be effective, patients must consume nutrient-dense diets foods. Patients should take caution on their carbohydrate’s intake, glycemic index, and glycemic load levels of foods they consume, this way they can control and maintain the blood glucose levels close to normal. Today, with the tremendous growth in technology we see an increase in the adoption and use of health recommender systems that are slowly becoming a close companion to an individual. A health recommender system can study the user, gather relevant information and recommend what suits the user best, hence making life easy. This study sought to develop a food recommender system expressly for diabetes Type 2 patients which will incorporate the use of a glucometer, a medical device used to assist patients and the caregivers monitor blood glucose levels and use that data to adjust nutritional therapy. Based on their sugar levels the recommender system will advise the patient on the appropriate foods they can consume at that time, ensuring the blood glucose target is attained hence reducing chances of sudden blood spikes and dips. Once the patient keys in the food they want to eat the model will respond by giving the patient the go-ahead to consume the food. In this study, we tested the Naïve Bayes algorithm with collaborative filtering to recommend food and achieved a prediction accuracy of 90.0%. The algorithm outperformed decision trees which gave an accuracy of 78% and the Support vector machine which had an accuracy of 75%.