Jan Warren-Findlow, Maren J Coffman, Erin Vinoski Thomas, Lisa M Krinner
ECHO: A Pilot Health Literacy Intervention to Improve Hypertension Self-Care
Health literacy research and practice
study evaluates efficacy voice analysis combined machine learning ml techniques enabling diagnosis parkinsons disease pd voice data phonation vowel three distinct datasets two university california irvine ml repository one figshare 432 participants 278 pd patients analyzed employed four ml modelsartificial neural networks random forest gradient boosting gb support vector machine svmalongside two ensemble methods soft voting classifierensemble voting classifier stacking methodensemble stacking model esm models underwent 50 iterations evaluation involving various data splits 10fold crossvalidation comparative analysis done using oneway analysis variance followed bonferroni posthoc corrections esm svm gb models emerged top performers demonstrating superior performance across metrics including accuracy sensitivity specificity precision f1 score area receiver operating characteristic curve roc auc despite data heterogeneity variable selection limitations models showed high values metrics ml integration voice analysis mainly esm svm gb promising early pd diagnosis using multisource data large sample size enhances findings validity reliability generalizability integrating advanced ml techniques voice analysis demonstrates substantial potential improving early pd detection offering valuable tools speechlanguage pathologists slps findings provide clinically relevant insights applied within scope slp practice refine diagnostic processes facilitate early intervention