Non-invasive risk prediction method for diabetes

• Main Authors: QI FENG, GAO RUIJUN, ZHANG BING, YAO BAITAO, ZHANG YUAN, GUO LICHUAN, SONG XIN, WANG CHAO, BAI JING
• Format: Patent
• Language: Chinese; English
• Published: 11.02.2022
• Subjects: CALCULATING; COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS; COMPUTING; COUNTING; DIAGNOSIS; HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATIONTECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING ORPROCESSING OF MEDICAL OR HEALTHCARE DATA; HUMAN NECESSITIES; HYGIENE; IDENTIFICATION; INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTEDFOR SPECIFIC APPLICATION FIELDS; MEDICAL OR VETERINARY SCIENCE; PHYSICS; SURGERY

The invention discloses a non-invasive risk prediction method for diabetes, which belongs to the field of intelligent medical treatment, and comprises the steps of recruiting subjects, and constructing a data set containing face images of diabetes patients and healthy people; carrying out image preprocessing; positioning feature points of the face image, cutting and splicing key areas, and marking corresponding diagnosis information; randomly dividing the marked sample data set into a training set, a verification set and a test set; constructing a residual attention network, and carrying out supervised machine learning on the samples; and performing model parameter adjustment according to the performance on the verification set, evaluating the generalization ability of the model based on the discrimination effect on the test set, and obtaining a risk prediction model with good performance by adopting a cross validation method. The risk prediction model constructed by the invention can carry out rapid, non-inv