Diabetes Detection Using Gradient Boosting Classifier (XGBOOST)
Keywords:
Diabetes, Machine, Learning, Prediction, DatasetAbstract
Diabetes results from elevated glucose levels in humans and should not be overlooked if left untreated, as it can lead to significant health issues, including heart complications, kidney disorders, hypertension, and eye damage, as well as impact other organs. Early detection of diabetes can help manage the condition effectively. To accomplish this, we aim to predict diabetes in individuals with high accuracy by utilizing various machine learning techniques. These techniques enhance prediction outcomes by developing models from patient data. In this research, we applied machine learning classification and ensemble methods to a dataset for diabetes prediction. The techniques used include K-Nearest Neighbor (KNN), Logistic Regression (LR), Support Vector Machine (SVM), Gradient Boosting (XGBOOST), LightGradientBoosting (LightGBM) and Random Forest (RF). Each model demonstrated varying levels of accuracy when compared to one another. This project identifies a model with superior accuracy, indicating its effectiveness in predicting diabetes. Our findings reveal that the Gradient Boosting Classifier (XGBOOST) method achieved greater accuracy than the other machine learning techniques
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