Effective Approach for Early Detection of Diabetes by Logistic Regression through Risk Prediction

Abstract

Heart disease, cancer, renal failure, eye damage, and blindness are just some of the complications that may result from uncontrolled diabetes. Scientists are inspired to develop a Machine Learning (ML) approach for diabetes forecasting. To improve illness diagnosis, medical personnel must make use of ML algorithms. Different ML algorithms for identifying diabetes risk at an early stage are examined and contrasted in this research. The goal in analysing diabetes prediction models is to develop criteria for selecting high-quality studies and synthesising the results from several studies. Nonlinearity, normality, correlation structure, and complexity characterise the vast majority of medical data, making analysis of diabetic data a formidable task. Algorithms based on machine learning are not permitted to be used in healthcare or medical imaging. Early diabetes mellitus prediction necessitates a strategy distinct from those often used. Diabetic patients and healthy individuals may be separated using a risk stratification approach based on machine learning. This study is highly recommended since it reviews a variety of papers that may be used by researchers working on diabetes prediction models.