Prediction of Type 2 Diabetes using logistic regression techniques

Abstract

Abstract Diabetes is recognized as a significant public health concern and a global epidemic. It is a chronic condition resulting from insufficient insulin production by the pancreas. The long-term elevated blood sugar levels associated with diabetes lead to chronic damage and impaired function in multiple tissues, such as the eyes, kidneys, heart, blood vessels, and nerves. The objective of this study is to demonstrate the utilization of machine-learning algorithms, specifically logistic regression, in predicting an individual's likelihood of having diabetes based on medical data. Furthermore, the study aims to develop a prediction model that determines whether a patient has diabetes by analyzing specific diagnostic measurements included in the dataset. Various techniques will be explored to enhance the performance and accuracy of the prediction model.   Results: The logistic regression algorithm for the dataset containing various patient data, found that the algorithm predicted whether people would be diagnosed with diabetes with an 82 percent success rate.