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          <dc:title>&lt;p&gt;Preliminary testing of the predictive ability of the basic modesl on the original dataset. Including we deploy Decision Tree, XGBoost, logistic regression, SVM, and a simple multi-layer neural network. At the same time, we also extracted the test set as a sample balanced version, and the performance of XGBoost and neural network models was acceptable.&lt;/p&gt;</dc:title>
          <dc:creator>Qiang Li (8118)</dc:creator>
          <dc:creator>Yangxinrong Tang (24950724)</dc:creator>
          <dc:creator>Yiling Shi (12091356)</dc:creator>
          <dc:creator>Yufeng Wu (567558)</dc:creator>
          <dc:creator>Zhan Xu (6239195)</dc:creator>
          <dc:creator>Hefeng Zhou (5049827)</dc:creator>
          <dc:creator>James J. Zhang (9144302)</dc:creator>
          <dc:subject>Biotechnology</dc:subject>
          <dc:subject>Biological Sciences not elsewhere classified</dc:subject>
          <dc:subject>Information Systems not elsewhere classified</dc:subject>
          <dc:subject>Cancer</dc:subject>
          <dc:subject>Science Policy</dc:subject>
          <dc:subject>using shapley values</dc:subject>
          <dc:subject>traditional approaches relying</dc:subject>
          <dc:subject>routine clinical use</dc:subject>
          <dc:subject>practical decision support</dc:subject>
          <dc:subject>incremental data updates</dc:subject>
          <dc:subject>experimental results demonstrate</dc:subject>
          <dc:subject>enhance early identification</dc:subject>
          <dc:subject>children &amp;# 8217</dc:subject>
          <dc:subject>become particularly urgent</dc:subject>
          <dc:subject>assisted clinical diagnosis</dc:subject>
          <dc:subject>772 samples ),</dc:subject>
          <dc:subject>novel data synthesis</dc:subject>
          <dc:subject>life factors associated</dc:subject>
          <dc:subject>adolescent depression risk</dc:subject>
          <dc:subject>reflections &lt;/ p</dc:subject>
          <dc:subject>imbalanced health data</dc:subject>
          <dc:subject>novel method</dc:subject>
          <dc:subject>life indicators</dc:subject>
          <dc:subject>risk youths</dc:subject>
          <dc:subject>risk stratification</dc:subject>
          <dc:subject>risk assessment</dc:subject>
          <dc:subject>social interactions</dc:subject>
          <dc:subject>sleep duration</dc:subject>
          <dc:subject>replacing clinician</dc:subject>
          <dc:subject>recent attempts</dc:subject>
          <dc:subject>predict survey</dc:subject>
          <dc:subject>national survey</dc:subject>
          <dc:subject>may miss</dc:subject>
          <dc:subject>issues brought</dc:subject>
          <dc:subject>isolation policies</dc:subject>
          <dc:subject>interpretability analysis</dc:subject>
          <dc:subject>factor replacement</dc:subject>
          <dc:subject>especially given</dc:subject>
          <dc:subject>conventional models</dc:subject>
          <dc:subject>average accuracy</dc:subject>
          <dc:subject>19 pandemic</dc:subject>
          <dc:subject>14 %.</dc:subject>
          <dc:description>&lt;p&gt;Preliminary testing of the predictive ability of the basic modesl on the original dataset. Including we deploy Decision Tree, XGBoost, logistic regression, SVM, and a simple multi-layer neural network. At the same time, we also extracted the test set as a sample balanced version, and the performance of XGBoost and neural network models was acceptable.&lt;/p&gt;</dc:description>
          <dc:date>2026-09-15T18:07:21Z</dc:date>
          <dc:type>Dataset</dc:type>
          <dc:type>Dataset</dc:type>
          <dc:identifier>10.1371/journal.pone.0357661.t003</dc:identifier>
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