The University of Osaka · 보건학
Hiroe Seto 교수의 연구실은 대규모 건강검진 데이터를 기반으로 한 기계학습 기반 질병 예측 모델 개발에 초점을 맞추고 있습니다. 특히 당뇨병 예측에서 경량 그라디언트 부스팅 머신(LightGBM)과 로지스틱 회귀 모델의 신뢰성 및 캘리브레이션 성능을 비교 분석하며, 예측 정확도 향상을 위한 고도화된 캘리브레이션 평가 방법을 개발하고 있습니다. 중점적으로 다루는 분야는 중심비만, 이상지질혈증, 인슐린 저항성 등 대사증후군의 계절적 변동성과 그 영향 요인 분석입니다.
표시된 성과는 수집된 데이터 기준으로 산출되며, 일부 차이가 있을 수 있습니다.
We sought to verify the reliability of machine learning (ML) in developing diabetes prediction models by utilizing big data. To this end, we compared the reliability of gradient boosting decision tree (GBDT) and logistic regression (LR) models using data obtained from the Kokuho-database of the Osaka prefecture, Japan. To develop the models, we focused on 16 predictors from health checkup data from April 2013 to December 2014. A total of 277,651 eligible participants were studied. The prediction
This finding, complex seasonal variations of MetS prevalence, WC, TG, and FPG, could not be derived from previous studies using just the mean values in spring, summer, autumn and winter or the cosinor analysis. More attention should be paid to factors affecting seasonal variations of central obesity, dyslipidemia and insulin resistance.
In developing risk prediction models for specific diseases, it is essential to evaluate the calibration performance of the prediction model. Various methods have been proposed to assess the calibration of prediction models, but it has been pointed out that conventional methods based on the predicted probability of the model are insufficient to detect miscalibration. Another problem is that a method for evaluating calibration for continuous variables of interest has not yet been established. We t
<title>Abstract</title> We sought to verify the reliability of machine learning (ML) in developing diabetes prediction models by utilizing big data. To this end, we compared the reliability of gradient boosting decision tree (GBDT) and logistic regression (LR) models using data obtained from the Kokuho-database of the Osaka prefecture, Japan. To develop the models, we focused on 16 predictors from health checkup data from April 2013 to December 2014. A total of 277,651 eligible participants were
<title>Abstract</title> In developing risk prediction models for specific diseases, it is essential to evaluate the calibration performance of the prediction model. Various methods have been proposed to assess the calibra- tion of prediction models, but it has been pointed out that conventional methods based on the predicted probability of the model are insufficient to detect miscalibration. Another problem is the inability to assess calibration for variables of interest, such as covariate of hi
