Diabetes Preventive Knowledge Management System for Recommending an Ice Cream to University Grads Based on Their Life Style and Eating Habits

Abstract

Lifestyle and eating habits with the special focus on young university grads are considered to design and develop a Knowledge Management System (KMS). An appropriate ice cream is suggested via KMS to university grads, which keeps blood glucose level in control and acts as a diabetes preventive KMS. Designed KMS is based on effective Data Science (DS), Big Data techniques considering standalone and proposed distributed versions of Analytical Hierarchy Process (AHP), Monte Carlo AHP (MC-AHP), Goal Programming (GP), K-Means and Artificial Neural Network (ANN) Clustering and Collaborative Filtering (CF). Incremental-learning gains and updates knowledge at each level of applied DS techniques. Developed KMS analyzed ice cream consumption pattern, lifestyle & health condition attributes of university students to promote a novel KM strategy in terms of ice cream recommendation and can give altogether novel trigger to health-conscious students. The confluence of health, students, ice creams and DS is achieved and discussed in this chapter.