Diet analysis of diabetes using machine learning

Abstract

The latest advancement in health sciences have prompted a need for creation of data, for example, health treatment information, produced in large volumes of health records. Machine learning techniques seems to be increasing every day, like never before, the motivation behind this work is to change all accessible data into significant data. Diabetes mellitus is a type of metabolic problem, creating an impact on human health around the world and the main cause for this is hereditary. Patients should know how much sugar content present in their meal and what provokes the sugar level. The motive of this thesis is to analyze the data and use machine learning to understand regarding 1) how spicy levels and natural sugars impacted sugar levels 2) how can a food impact sugar levels 3) Comparison of results with different classification algorithms. The best accuracy was obtained from SVM to classify the sugar level present in food.