Prediction and Recommendation System for Diabetes Using Machine Learning Models

Abstract

Diabetes is one of the most deadly diseases on the planet. It is also a cause of a variety of illnesses, such as coronary artery disease, blindness, and urinary organ disease. In this situation, the patient must visit a medical center to obtain their results following consultation. Finding the right combination of characteristics and machine learning techniques for classification is also very critical. However, with the advancement of machine learning techniques, we now have the potential to find a solution to the current problem. The healthcare recommendation system (HRS) may be designed to predict health by evaluating patient lifestyle, physical health, mental health aspects using machine learning. For example, training the model using people's age and diabetes helps to predict new patients without a specific diagnostic for diabetes. The proposed deep learning model with convolutional neural network (D-CNN) achieves an overall accuracy of 96.25%. D-CNN is found to be more successful for diabetes prediction than other machine learning (ML) approaches in the experimental analysis.

Integrating Machine Learning for Accurate Prediction of Early Diabetes

“…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.…”