首页|Northwest University Reports Findings in Machine Learning (Evaluation of salivary glycopatterns based diagnostic models for prediction of diabetic vascular complications)
Northwest University Reports Findings in Machine Learning (Evaluation of salivary glycopatterns based diagnostic models for prediction of diabetic vascular complications)
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New research on Machine Learning is the subject of a report. According to news reporting out of Xi’an, People’s Republic of China, by NewsRx editors, research stated, “Diabetic vascular complications (DVC) are the main cause of death in diabetic patients. However, there is a lack of effective biomarkers or convenient methods for early diagnosis of DVC.” Our news journalists obtained a quote from the research from Northwest University, “In this study, the salivary glycopatterns from 130 of healthy volunteers (HV), 139 patients with type 2 diabetes mellitus (T2DM) and 167 patients with DVC were case-by-case analyzed by using lectin microarrays. Subsequently, diagnostic models were developed using logistic regression and machine learning algorithms based on the data of lectin microarrays in training set. The performance of diagnostic models was evaluated in an independent blind cohort. The results of lectin microarrays indicated that the glycopatterns identified by 16 lectins (e.g. BS-I, PWM and EEL) were significantly altered in DVC patients compared with patients with T2DM, which suggested the alterations in salivary glycopatterns could reflect onset of DVC. Notably, K-Nearest Neighbor (KNN) model exhibited better performance for distinguishing DVC (accuracy: 0.939) than other models in blind cohort. The integrated classifier, which combined three machine learning models, exhibited a higher overall accuracy ( 0.933) than other models in blind cohort.”
Xi’anPeople’s Republic of ChinaAsiaCyborgsDiagnostics and ScreeningEmerging TechnologiesHealth and MedicineMachine Learning
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