Diabetes Risk Prediction Using Extreme Gradient Boosting (XGBoost)

Kartina Diah Kusuma Wardhani; Memen Akbar

doi:10.15575/join.v7i2.970

Diabetes Risk Prediction Using Extreme Gradient Boosting (XGBoost)

Kartina Diah Kusuma Wardhani, Memen Akbar

Abstract

One of the uses of medical data from diabetes patients is to produce models that can be used by medical personnel to predict and identify diabetes in patients. Various techniques are used to be able to provide a diabetes model as early as possible based on the symptoms experienced by diabetic patients, including using machine learning. The machine learning technique used to predict diabetes in this study is extreme gradient boosting (XGBoost). XGBoost is an advanced implementation of gradient boosting along with multiple regularization factors to accurately predict target variables by combining simpler and weaker model set estimations. Errors made by the previous model are tried to be corrected by the next model by adding some weight to the model. The diabetes prediction model using XGBoost is shown in the form of a tree, with the accuracy of the model produced in this study of 98.71%

Keywords

Diabetes; Prediction; Machine Learning; XGBoost

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