Can feature structure improve model's precision? A novel prediction method using artificial image and image identification.
JAMIA Open
; 7(1): ooae012, 2024 Apr.
Article
in En
| MEDLINE
| ID: mdl-38348347
ABSTRACT
Objectives:
This study aimed to develop an approach to enhance the model precision by artificial images. Materials andMethods:
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 10â000 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.
Full text:
1
Collection:
01-internacional
Database:
MEDLINE
Type of study:
Diagnostic_studies
/
Prognostic_studies
/
Risk_factors_studies
Language:
En
Journal:
JAMIA Open
Year:
2024
Document type:
Article
Affiliation country:
Country of publication: