The Hemoglobin Prediction Modeling Based on the National Health Data

Purpose: Leveraging on the contemporary machine learning algorithms, we would like to improve the prediction performance of the existing MLR(Multiple Linear Regression) model to predict the blood hemoglobin levels.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.