| 국민 건강 검진 데이터 기반 혈색소(헤모글로빈) 예측 모델링 |
| 정대원, 황욱연 |
| 동아대학교글로벌비즈니스학과 |
| The Hemoglobin Prediction Modeling Based on the National Health Data |
| Dae Won Jung, Wook-Yeon Hwang |
| Department of Global Business, Dong-A University |
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Received: August 23, 2024; Revised: September 19, 2024 Accepted: September 25, 2024. Published online: December 31, 2024. |
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| ABSTRACT |
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. |
| Key Words:
The National Health Data, Blood Hemoglobin Levels, Machine Learning Algorithms, Prediction Model |
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