Diabet Prediction with Logistic Regression

Abstract

Diabetes is a common chronic disease that seriously challenges countries worldwide. However, there are problems such
as low detection rate, low awareness rate, low acceptance rate, and low treatment compliance rate in the diagnosis and
treatment process. This paper aims to construct a Logistic Regression Model to predict whether a patient has diabetes
or not and to investigate the key factors that can be used to diagnose whether a patient has diabetes or not to promote
the development of diabetes treatment. The research results show that the patient’s characteristics, such as the number
of times prior, body mass index, and diagnostic measurements such as plasma glucose concentration 2 hours in an
oral glucose tolerance test, trips skin fold thickness, 2-hour serum insulin, and diabetes pedigree function will have a
significant impact on whether the patient has diabetes. Therefore, the above six variables should be focused on when
diagnosing diabetes mellitus.