국민건강검진데이터기반혈색소(헤모글로빈) 예측모델링 = The Hemoglobin Prediction Modeling Based on the National Health Data

다국어 초록 (Multilingual Abstract)

Purpose: Leveraging on the contemporary machine learning algorithms, we would like to improve the prediction
performance of the existing MLR(MultipleLinearRegression)modeltopredictthebloodhemoglobinlevels.
Methods: The GBDT(Gradient Boosting Decision Trees) such as the XGBoost(Extreme Gradient Boosting),
the LightGBM(Light Gradient Boosting Machine), and the CatBoost(Categorical Boost), the RF(Random
Forests), and the MLP(Multi-Layer Perceptron) are adopted to build the new prediction models.
Results: The machine learning algorithms provide prediction performance better than the existing prediction
model.
Conclusion: The proposed prediction models can be considered as an alternative better than the existing
prediction model.