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Design-Oriented Thinking in STEM education: Exploring the Impact on Preschool Children's Twenty-First-Century Skills
Science & education
application datadriven methods expected play increasingly important role healthcare however lack personnel necessary skills develop models interpret output preventing wider adoption methods address gap introduce describe orientate software automated application machine learning classification algorithms clinical practitioners lacking specific technical skills orientate allows selection features target variable automatically generates number classification models crossvalidates finding best model evaluating also implements custom feature selection algorithm systematic searches best combination predictors given target variable finally outputs comprehensive report graphs facilitates explanation classification model results using global interpretation methods interface prediction new input samples feature relevance interaction plots provided orientate allow use statistical inference replace andor complement classical statistical studies application dataset healthy special health care needs shcn children treated deep sedation discussed case study example dataset despite small size feature selection algorithm found set features able predict need second sedation f1 score 083 roc auc 092 eight predictive factors populations found ordered relevance assigned model discussion derive inferences relevance interaction plots comparison classical study also provided orientate automatically finds suitable features generates accurate classifiers used preventive tasks addition researchers without specific skills data methods use application machine learning classification complement classical studies inferential analysis features case study high prediction accuracy second sedation shcn children achieved analysis relevance features showed number teeth pulpar treatments first sedation predictive factor second sedation