Hybridization of bio-inspired algorithms with machine learning models for predicting the risk of type 2 diabetes mellitus

Abstract

Type 2 diabetes mellitus is a chronic condition that affects millions of people worldwide. Predicting the risk of developing this disease is critical for early intervention and prevention. Bio-inspired algorithms and machine learning models have shown promising results in predicting the risk of type 2 diabetes mellitus. In this paper, we will explore the use of these two approaches and their hybridization to improve the accuracy of risk prediction. The first section will introduce bio-inspired algorithms and their application in predicting the risk of type 2 diabetes mellitus. We will discuss the advantages of using these algorithms and their limitations. The second section will focus on machine learning models and their potential in predicting the risk of type 2 diabetes mellitus. We will also discuss the limitations of this approach. The final section will compare and contrast the two approaches and explore how their hybridization can overcome their limitations and improve the accuracy of risk prediction. Overall, this paper aims to provide an in-depth analysis of the use of bio-inspired algorithms and machine learning models in predicting the risk of type 2 diabetes mellitus and their hybridization to improve their accuracy.