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인공지능과 모발의 필수 미네랄 원소 함량을 이용한 피험자 연령 예측

Prediction of Hair Owners' Age using Hair Mineral Content and Artificial Intelligence

  • 투고 : 2022.09.20
  • 심사 : 2022.11.01
  • 발행 : 2022.11.30

초록

모발의 필수 미네랄 원소 농도 데이터로 인공지능을 학습시킨 후, 피험자의 모발 미네랄 원소 농도로 나이를 예측하도록 하고 그 결과를 피험자의 실제 나이와 비교하여 연관성을 조사하였다. 전체 모발 데이터는 296개이었으며그 중 2/3를 인공지능 학습에 그리고 1/3을 피험자 데이터로 사용하였다. 25세 이하의 성장기 피험자의 실제 나이와 인공지능이 예측한 나이 사이에는 0.678 의 중상 정도의 상관관계가 있었다. 중년의 피험자 그룹에서는 연관성이 거의 없었고 노년의 피험자 그룹에서는 0.522의 약한 상관관계를 보였다. 모발의 미네랄 원소 농도 데이터를 이용한 인공지능의 유용성을 확보하기 위해서는 더 많은 수의 데이터를 제공하여 인공지능을 학습시키는 과정이 필요하다.

After artificial intelligence was trained with the data on the concentration of essential mineral elements in hair, the age was predicted by the concentration of mineral elements in the hair of the subject, and the result was compared with the actual age of the subject, and the correlation was investigated. The total number of hair data was 296, of which 2/3 were used for AI learning and 1/3 was used as the subject data. There was a correlation of 0. 678 between the actual age of the young subjects under the age of 25 and the age predicted by the AI. There was almost no correlation in the middle-aged subjects group, and there was a weak correlation of 0.522 in the elderly subject group. In order to secure the usefulness of artificial intelligence using hair mineral element concentration data, it is necessary to provide a larger number of data to the artificial intelligence.

키워드

과제정보

이 논문은 2022년도 을지대학교 대학혁신 지원사업 지원으로 연구되었음.

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