The Use of Machine Learning Techniques to Predict Diabetes in Patients with Cystic Fibrosis

Abstract

The accuracy of the prognosis of diabetes in patients with cystic fibrosis is crucial, as it highly connected with mortality and other complications. The prognosis of diabetes is a time-consuming process. Usually, it is performed by medical staff and can often lead to misdiagnosis. The aim of the study was to analyze and evaluate risk factors of developing diabetes in patients diagnosed with Cystic Fibrosis by using classification machine learning techniques. The ECFS data register was used to train and test the models. Visualization of our results using SHAP values highlights that most important features are age, antibiotic treatment, FEV1 value and lung transplant as risk predictors for diabetes.