Search alternatives:
feature optimization » resource optimization (Expand Search), feature elimination (Expand Search), structure optimization (Expand Search)
codon optimization » wolf optimization (Expand Search)
data feature » data figure (Expand Search), each feature (Expand Search), a feature (Expand Search)
binary data » primary data (Expand Search), dietary data (Expand Search)
feature optimization » resource optimization (Expand Search), feature elimination (Expand Search), structure optimization (Expand Search)
codon optimization » wolf optimization (Expand Search)
data feature » data figure (Expand Search), each feature (Expand Search), a feature (Expand Search)
binary data » primary data (Expand Search), dietary data (Expand Search)
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121
Table 1_Heavy metal biomarkers and their impact on hearing loss risk: a machine learning framework analysis.docx
Published 2025“…Demographic, clinical, and heavy metal biomarker data (e.g., blood lead and cadmium levels) were analyzed as features, with hearing loss status—defined as a pure-tone average threshold exceeding 25 dB HL across 500, 1,000, 2000, and 4,000 Hz in the better ear—serving as the binary outcome. …”
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122
Machine Learning-Ready Dataset for Cytotoxicity Prediction of Metal Oxide Nanoparticles
Published 2025“…</p><p dir="ltr"><b>Applications and Model Compatibility:</b></p><p dir="ltr">The dataset is optimized for use in supervised learning workflows and has been tested with algorithms such as:</p><p dir="ltr">Gradient Boosting Machines (GBM),</p><p dir="ltr">Support Vector Machines (SVM-RBF),</p><p dir="ltr">Random Forests, and</p><p dir="ltr">Principal Component Analysis (PCA) for feature reduction.…”