당뇨병성 신장질환(DKD)의 조기 진단을 위해 바이오마커 FN1과 ALDH2가 제안되었으나, 기존 진단모델은 배치효과 보정을 단일 기법으로만 적용하고 서로 다른 분류 모델을 결합한 앙상블 기법은 고려하지 않았다. 본 연구는 DKD와 정상군 간 이진 진단에서 4가지 보정 방식을 비교하고, 예측이 서로 다른 모델을 결합할수록 앙상블 효과가 크다는 점에 착안해 학습 방식이 다른 GLM과 Naive Bayes(NB)를 결합한 앙상블 모델을 제안한다. 실험 결과, 코호트 간 평균과 분산을 함께 맞추는 cohort_z가 가장 우수하였으며, 제안 모델은 AUC 0.971로 선행 연구 GLM 모델의 AUC 0.942를 넘어섰다. 이는 적절한 보정 위에서 GLM과 NB의 결합이 진단 성능을 개선할 수 있음을 보여주며, 오믹스 기반 질환 진단모델에도 활용될 수 있다.
KSP Keywords
Naive Bayes
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