Development of a novel method for prediction using artificial image and image identification

2023 Fiscal Year Final Research Report

• Project/Area Number: 22K21186
• Research Category: Grant-in-Aid for Research Activity Start-up
• Allocation Type: Multi-year Fund
• Review Section: 0908:Society medicine, nursing, and related fields
• Research Institution: Fujita Health University
• Principal Investigator: He Yupeng 藤田医科大学, 医学部, 助教 (00953267)
• Project Period (FY): 2022-08-31 – 2024-03-31
• Keywords: Artificial image / 人工画像 / 疫学研究 / 予測モデル / 機械学習

Outline of Final Research Achievements

A novel method using artificial image was developed to enhance the model precision in epidemiology study. The concept was inspired from image identification. Pixels in digital images are used as features when training the identification model. The order-related relationship is assumed to exist in epidemiological data. Given a certain dataset, features are transformed to pixel values for generating artificial images. Orders of pixels are randomly permutated and the model is trained using pixel-permutated artificial image sample sets. In the preliminary experiment, one binary response was designed to be predicted by 76 features. 10,000 artificial image sample sets were randomly selected for model training. Models’ performance (area under the receiver operating characteristic curve values) depicted a bell-shaped distribution. Namely, feature order information had a strong impact on model performance. Our novel model construction strategy has potential to enhance model predictability.

Free Research Field

Public Health

Academic Significance and Societal Importance of the Research Achievements

従来の疫学研究でよく使われる線形モデルと比較して、本研究で開発した新手法は、特徴を2次元人工画像の形式で配置することで、１）モデルの精度を向上させる。２）複数の特徴間の交絡要因を究明できる。３）ブラックボックスのような機械学習モデルを視覚的に説明できる。４）特徴の位置を使用して特徴の重要性を説明する。５）疫学調査以外の順序不特定のデータの分析に活用できる。