Aklile Semu Tefera, Ermiyas Endewunet Melaku, Besufekad Mulugeta Urgie, Erzik Muhammed Hassen, Tilahun Deresse Tamene, Enguday Demeke Gebeyaw
Barriers to implementing problem-based learning at the school of medicine of Debre Berhan University, Ethiopia
BMC medical education
metabolomics wealth data offers valuable avenue enhancing predictions decisionmaking diabetes observational study aimed leverage machine learning ml algorithms predict 4year risk developing type 2 diabetes mellitus t2dm using targeted quantitative metabolomics data cohort 279 cardiovascular risk patients underwent coronary angiography initially free t2dm according american diabetes association ada criteria analyzed baseline including anthropometric data targeted metabolomics using liquid chromatography lcmass spectroscopy ms flow injection analysis fiams respectively patients followed four years time 115 patients developed t2dm data preprocessing 362 variables used ml employing caret package r dataset divided training test sets 7525 ratio used oversampling approach address classifier imbalance t2dm incidence additional recursive feature elimination step identifying set 77 variables valuable model generation support vector machine svm model linear kernel demonstrated promising predictive capabilities exhibiting f1 score 50 specificity 93 balanced unbalanced accuracies 72 88 respectively topranked features bile acids ceramides amino acids hexoses whereas anthropometric features age sex waist circumference body mass index contribution conclusion ml analysis metabolomics data promising tool identifying individuals risk developing t2dm opens avenues personalized early intervention strategies