Integrating Machine Learning for Accurate Prediction of Early Diabetes: A Novel Approach

2. Literature Review

Diabetes mellitus is a chronic metabolic disorder characterized by elevated blood glucose levels resulting from either insufficient insulin production or inadequate utilization of insulin by the body. Early detection and prediction of diabetes are crucial for effective management and prevention of complications associated with the disease. In recent years, machine learning (ML) has emerged as a powerful tool in the field of healthcare, offering the potential to improve the accuracy of early diabetes prediction (Ahmed et al. 2021; Birjais et al. 2019; Laila et al. 2022; Malviya, Dave, and Kailash Chandra Bandhu 2023; Mustary and Singamsetty 2022; Pandey, Litoriya, and Pandey 2020; Parimala, Kayalvizhi, and Nithiya 2023). This literature review aims to provide an overview of the current state of research on integrating machine learning techniques for accurate prediction of early diabetes, focusing on recent developments and novel approaches in the field.

The motive is to link Machine Learning approaches with medical data to enable the algorithm to increase its efficiency of diabetes diagnosis (Dutta and Bandyopadhyay 2021)(Srivastava, Kumar, and Singh 2020).

Advanced analytics is gaining a strong reputation in the rapidly developing field of big data. There is a huge amount of information available about diseases, their symptoms, and effects on good physical condition. However, this collection of information is not always properly inspected to forecast or investigate a condition. The main motive is to provide an elaborative explanation of predictive approaches, including different types of predictive models, methods for developing them, and their applications in diabetes treatment and other areas of healthcare (Jayanthi, Babu, and Rao 2017).