Manali I Patel, Madhuri Agrawal, Douglas W Blayney, M Kate Bundorf, Arnold Milstein
Long-Term Engagement of Patients With Advanced Cancer: Results From the EPAC Randomized Clinical Trial
JAMA oncology
global burden multiple myeloma mm increasing every year developed machine learning models provide reference early detection mm total 465 patients 150 healthy controls enrolled retrospective study based variable screening strategy least absolute shrinkage selection operator lasso three prediction models logistic regression lr support vector machine svm random forest rf established combining complete blood count cbc cell population data cpd parameters training set 210 cases verified validation set 90 cases test set 165 cases performance model analyzed using receiver operating characteristic roc curve calibration curves decision curve analysis dca accuracy sensitivity specificity positive predictive value negative predictive value area roc curve auc applied evaluate models delong test used compare auc models six parameters including rbc 1012l rdwcv ig newz lywx lywz screened lasso construct model among three models auc rf model training set validation set test set 0956 0892 0875 higher lr model 0901 0849 0858 svm model 0929 0868 0846 delong test showed significant differences among models training set significant differences validation set significant differences svm rf models test set calibration curve dca showed three models good validity feasibility rf model performed best proposed rf model may useful auxiliary tool rapid screening mm patients