국민건강검진데이터기반혈색소(헤모글로빈) 예측모델링
The Hemoglobin Prediction Modeling Based on the National Health Data
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초록
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.
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.
참고문헌 (22)
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