Advanced Industrial SCIence (산업과 과학)

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Big Data Management in Structured Storage Based on Fintech Models for IoMT using Machine Learning Techniques

기계학습법을 이용한 IoMT 핀테크 모델을 기반으로 한 구조화 스토리지에서의 빅데이터 관리 연구

  • Kim, Kyung-Sil (Dept. of Software convergence, Graduate School of Baekseok University)
  • 김경실 (백석대학교 일반대학원 소프트웨어융합학부)
  • Received : 2022.08.23
  • Accepted : 2022.09.23
  • Published : 2022.09.30
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Abstract

To adopt the development in the medical scenario IoT developed towards the advancement with the processing of a large amount of medical data defined as an Internet of Medical Things (IoMT). The vast range of collected medical data is stored in the cloud in the structured manner to process the collected healthcare data. However, it is difficult to handle the huge volume of the healthcare data so it is necessary to develop an appropriate scheme for the healthcare structured data. In this paper, a machine learning mode for processing the structured heath care data collected from the IoMT is suggested. To process the vast range of healthcare data, this paper proposed an MTGPLSTM model for the processing of the medical data. The proposed model integrates the linear regression model for the processing of healthcare information. With the developed model outlier model is implemented based on the FinTech model for the evaluation and prediction of the COVID-19 healthcare dataset collected from the IoMT. The proposed MTGPLSTM model comprises of the regression model to predict and evaluate the planning scheme for the prevention of the infection spreading. The developed model performance is evaluated based on the consideration of the different classifiers such as LR, SVR, RFR, LSTM and the proposed MTGPLSTM model and the different size of data as 1GB, 2GB and 3GB is mainly concerned. The comparative analysis expressed that the proposed MTGPLSTM model achieves ~4% reduced MAPE and RMSE value for the worldwide data; in case of china minimal MAPE value of 0.97 is achieved which is ~ 6% minimal than the existing classifier leads.

사물인터넷(IoT) 기술은 최근 의료사물인터넷(IoMT)으로 정의된 대량의 의료 데이터를 처리하여 발전을 위해 개발된 의료분야에서 많이 활용되고 있다. 수집된 광범위한 의료 데이터는 수집된 의료 데이터를 처리하기 위해 구조화된 방식으로 클라우드에 저장된다. 그러나 방대한 양의 의료 데이터를 효과적으로 처리하는 것은 쉽지 않기 때문에 의료분야 구조 데이터를 개발하는 것이 필요하다. 본 논문에서는 IoMT에서 수집된 구조화된 건강 관리 데이터를 처리하기 위한 기계 학습 모드를 개발하였다. 광범위한 의료 데이터를 처리하기 위해 본 논문에서는 의료 데이터 처리를 위한 MTGPLSTM 모델을 제안하였다. 제안된 모델은 의료 정보 처리를 위한 선형 회귀 모델을 통합한다. 개발된 모델 이상치 모델은 IoMT에서 수집된 COVID-19 의료 데이터들의 평가 및 예측을 위해 FinTech 모델을 기반으로 구현되었다. 제안된 MTGPLSTM 모델은 감염 확산 방지를 위한 계획 계획을 예측하고 평가하기 위한 회귀 모델로 구성된다. 개발된 모델 성능은 LR, SVR, RFR, LSTM 및 제안된 MTGPLSTM 모델과 같은 서로 다른 분류기를 고려하였으며 1GB, 2GB, 3GB 등 데이터 크기가 다르다는 점도 주요하게 고려되었다. 제안된 MTGPLSTM 모델이 전 세계 데이터에 대해 최대 4% 감소된 MAPE 및 RMSE 값을 달성하였고 중국의 경우 기존 분류기보다 최대 6% 최소인 최소 MAPE(0.97)이 달성되었다.

Keywords

References

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