A Study on Predictive Modeling of I-131 Radioactivity Based on Machine Learning

• 영문명: A Study on Predictive Modeling of I-131 Radioactivity Based on Machine Learning
• 발행기관: 대한방사선과학회(구 대한방사선기술학회)
• 저자명: 유연욱 이충운 김정수
• 간행물 정보: 『방사선기술과학』제46권 제2호, 131~139쪽, 전체 9쪽
• 주제분류: 의약학 > 방사선과학
• 파일형태: PDF
• 발행일자: 2023.04.30

영문 초록

High-dose I-131 used for the treatment of thyroid cancer causes localized exposure among radiology technologists handling it. There is a delay between the calibration date and when the dose of I-131 is administered to a patient. Therefore, it is necessary to directly measure the radioactivity of the administered dose using a dose calibrator. In this study, we attempted to apply machine learning modeling to measured external dose rates from shielded I-131 in order to predict their radioactivity. External dose rates were measured at 1 m, 0.3 m, and 0.1 m distances from a shielded container with the I-131, with a total of 868 sets of measurements taken. For the modeling process, we utilized the hold-out method to partition the data with a 7:3 ratio (609 for the training set:259 for the test set). For the machine learning algorithms, we chose linear regression, decision tree, random forest and XGBoost. To evaluate the models, we calculated root mean square error (RMSE), mean square error (MSE), and mean absolute error (MAE) to evaluate accuracy and R2 to evaluate explanatory power. Evaluation results are as follows. Linear regression (RMSE 268.15, MSE 71901.87, MAE 231.68, R2 0.92), decision tree (RMSE 108.89, MSE 11856.92, MAE 19.24, R2 0.99), random forest (RMSE 8.89, MSE 79.10, MAE 6.55, R2 0.99), XGBoost (RMSE 10.21, MSE 104.22, MAE 7.68, R2 0.99). The random forest model achieved the highest predictive ability. Improving the model’s performance in the future is expected to contribute to lowering exposure among radiology technologists.

목차

Ⅰ. Introduction

Ⅱ. Materials and methods

Ⅲ. Results

Ⅳ. Discussion

Ⅴ. Conclusion

REFERENCES

키워드

공간선량률 머신러닝 의사결정트리 랜덤포레스트 External Dose Rate Machine Learning Decision Tree Random Forest XGBoost