Diabetes Risk Prediction using Logistic Regression Algorithm - Qatrunnada Refa Cahyani; Mochammad Januar Finandi; Jathu Rianti; Devi Lestari Arianti; Arya Dwi Pratama Putra (Yayasan Literasi Sains Indonesia, 2022)
Many factors affect people suffering from diabetes, some of which are high blood pressure, excess sugar levels, weight, genetic history of diabetes, age, number of pregnancies, skin fold thickness, and the amount of insulin levels in the body. Logistic regression is a statistical tool that can be used in classification modeling about the presence or absence of diabetes. The aim of this study is to predict diagnostically whether a patient has diabetes or not. The results obtained are relatively low predictions because the ranges of values ​​of several factors that cause it are very far apart so normalization is carried out so that the ranges of values ​​are close together. The result is that diabetes risk prediction using a logistic regression algorithm with normalization resulted in a recall of 55% while without normalization it was 43%. Thus, normalization can improve the performance of diabetes risk prediction using a logistic regression algorithm. This model is expected to be a reference for the treatment of diabetics for doctors in hospitals and in the community to find out how to maintain a lifestyle and how to avoid diabetes in terms of the variables that affect the occurrence of the disease.