A decision support system for diagnosis of diabetes using Logistic regression model

Abstract: The Healthcare industry contains very large and sensitive data and needs to be handled very carefully. Diabetes is one of the growing, fatal diseases all over the world. Medical professionals want a reliable prediction system to diagnose Diabetes. Different machine learning techniques are useful for examining the data from diverse perspectives and synopsizing it into valuable information. The accessibility and availability of huge amounts of data will be able to provide us with useful knowledge if certain data mining techniques are applied to it. The main goal is developing a decision support system for the diagnosis of diabetes using a machine learning model. Diabetes contributes to heart disease, kidney disease, nerve damage and blindness. So, efficiently mining the diabetes data is a crucial concern. The data mining techniques and methods will be discovered to find the appropriate approaches and techniques for efficient classification of the Diabetes dataset and in extracting valuable patterns. The RStudio software was employed for diagnosing diabetes. The Rivers State University Teaching Hospital dataset was acquired from the Health Database of the Hospital used for analysis. The dataset was studied and analyzed to build an effective model that predicts and diagnoses diabetes disease. In this study, we aim to apply the bootstrapping resampling technique to enhance the accuracy and the applying Logistic regression model.

Keywords: Logistic Regression Classification, Healthcare, Diabetes.