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 beenproposed to predict such disease. Those models only determine the disease label, leaving the likelihood of developing the diseaseunclear. Proposing a model for predicting the progression of disease becomes essential. Therefore, this article proposes a logisticregression model to anticipate the likelihood of Diabetes syndrome incidence. The model exploit capabilities of logistic regressionby using sigmoid function. The model's performance was evaluated using the Pima Indians Diabetes dataset and demonstratedhigh accuracy, sensitivity, and specificity. The prediction accuracy rate was 77.6%, with a sensitivity of 72.4%, specificity of79.6%, Type I Error of 27.6%, and Type II Error of 20.4%. Furthermore, the model indicates the feasibility of using laboratorytests, such as Pregnancies, Glucose, Blood Pressure, BMI, and DiabetesPedigreeFunction, to predict disease progress. Theproposed model can aid patients and physicians in understanding the disease's progression and implementing timely interventions