Predicting Diabetes Disease Occurrence Using Logistic Regression: An Early Detection Approach

Abstract

Diabetes disease is prevalent worldwide, and predicting its progression is crucial. Several model have been proposed to predict such disease. Those models only determine the disease label, leaving the likelihood of developing the disease unclear. Proposing a model for predicting the progression of disease becomes essential. Therefore, this article proposes a logistic regression model to anticipate the likelihood of Diabetes syndrome incidence. The model exploit capabilities of logistic regression by using sigmoid function. The model's performance was evaluated using the Pima Indians Diabetes dataset and demonstrated high accuracy, sensitivity, and specificity. The prediction accuracy rate was 77.6%, with a sensitivity of 72.4%, specificity of 79.6%, Type I Error of 27.6%, and Type II Error of 20.4%. Furthermore, the model indicates the feasibility of using laboratory tests, such as Pregnancies, Glucose, Blood Pressure, BMI, and DiabetesPedigreeFunction, to predict disease progress. The proposed model can aid patients and physicians in understanding the disease's progression and implementing timely interventions

Keywords

• Keywords: Diabetes disease, Machine learning, logistic Regression, diabetes prediction