Classification of Gestational Diabetes Using Machine Learning Techniques

Abstract

In this work, we characterized the association of diabetes with pregnancy and developed a classification system for gestational diabetes based on machine learning models. Using statistical data, we can predict whether or not a patient has gestational diabetes based on certain attributes: age, number of pregnancies, insulin levels, etc. This allows the patient to seek medical attention quickly to reduce the risk of complications from this disease on her health. To achieve this goal, we proposed a method of data classification using supervised learning techniques: KNN, decision tree, and Random Forest. First, we will use Machine Learning tools (Google Colab) to create and train our algorithm, then evaluate its effectiveness and accuracy. In the second phase, we will use our model to predict cases of diabetes.