A Web-Based Diabetes Prediction Application Using XGBoost Algorithm

Abstract

One of the diseases that is generally characterized by symptoms of an increase in glucose levels in the blood and is one of the body diseases classified as chronic is diabetes. Diabetes suffered by a person from time to time can cause serious damage to other organs such as blood vessels, kidneys, heart and nerves. Machine learning provides various data mining algorithms that can be used to assist medical experts. The accuracy of machine learning algorithms is a measure of the effectiveness of decision support systems. Prediction of diabetes can be seen from the patient's medical record data, therefore the author wants to create a diabetes prediction system independently through a website-based application system. This application system will be combined with data observation, namely the science of data mining using the XGBoost algorithm. The dataset is divided into training data by 80% and testing data by 20%. Before the data modeling was carried out, we carried out various parameter setting scenarios with the hope of evaluating and evaluating the implementation to be applied, the parameters we adjusted were colsample_bytree, gamma, learning_rate, max_depth, n_estimators, reg_alpha, reg_lambda, and subsample. After sharing the data and tuning parameters, the resulting model by applying the XGBoost algorithm has an accuracy of 74.67%, the resulting precision value is 57.40%, the resulting recall value is 65.94%, the resulting specificity value is 78, 50%.