Smart City Citizen Diabetes Predictions using Machine Learning

Abstract

Health and wellness is one of the most essential need for creating smart city wellbeing environment and promoting quality of life of smart city citizens. Growing urbanisation, aging population and rapid industrialization demands strategic implementation of healthcare measures for the smart city populations[1]. Deployment of heath parameter observatory measures is one of the most common initiatives, that can be easily incorporated within the smart city healthcare system. This research explores diabetics as a basis and evaluation of its associated parameter such as blood sugar, hemoglobin, body mass index for understanding the relationship between health data, poor health parameters and quality of life of smart city citizens [2]. Information and communication technology can play a significant role in this efforts. Predictive analytics using machine learning is one of such derivatives of information and communication technology. Hence, machine learning can facilitates prediction of associated healthcare parameters, there by enables decision making on health and wellness of smart city citizen easier [3]. This research also identifies, evaluates and explore various machine learning techniques for predicting health related issues and deceases. The research paper further highlight future scopes research with reference to decisions on healthcare and well being of smart city citizens.