Personalized Smart Diabetic System Using Hybrid Models of Neural Network Algorithms

Abstract

The healthcare sector is rapidly evolving due to the exponential growth of the digital space and emerging technologies. Maintaining and effectively handling large quantities of data has become difficult in all industries. Furthermore, collecting helpful knowledge from extensive data collection is a daunting challenge. There would be an immense amount of data that continues to grow, making it harder and harder to find some helpful information. In the healthcare industry, big data analytics offers a variety of tools and strategies for detecting or predicting illnesses faster and delivering better healthcare facilities to the right patient at the right time to increase the quality of life. It is not as simple as one would imagine, given the myriad functional challenges that need to be addressed within current health data analytics systems that offer procedural frameworks for data collection, aggregation, processing, review, simulation, and interpretation. This chapter aims to design a long-term, commercially viable, and intelligent diabetes diagnosis approach with tailored care. Due to a lack of systematic studies in the previous literature, this chapter describes the different computational methods used in big data analytical techniques and the various phases and modules that transform the healthcare economy from data collection to knowledge distribution. The investigation findings indicate that the suggested framework will effectively offer adapted evaluation and care advice to patients, emphasizing a knowledge exchange approach and adapted data processing model for the smart diabetic system.