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Can feature structure improve model's precision? A novel prediction method using artificial image and image
Yupeng He1, Qiwen Sun2, Masaaki Matsunaga1
1Department of Public Health, Fujita Health University School of Medicine, Toyoake, Aichi 4701192, Japan.
Objectives:
This study aimed to develop an approach to enhance the model precision by artificial images.
Materials And Methods:
Given an epidemiological study designed to predict 1 response using f features with M samples, each feature was converted into a pixel with certain value. Permutated these pixels into F orders, resulting in F distinct artificial image sample sets. Based on the experience of image recognition techniques, appropriate training images results in higher precision model. In the preliminary experiment, a binary response was predicted by 76 features, the sample set included 223 patients and 1776 healthy controls.
Results:
We randomly selected 10 000 artificial sample sets to train the model. Models' performance (area under the receiver operating characteristic curve values) depicted a bell-shaped distribution.
Conclusion:
The model construction strategy developed in the research has potential to capture feature order related information and enhance model predictability.
