archive (10).zip
Use Cases
Multiclass Disease Classification: Predict which deficiency disease a patient has based on symptoms and lab values Early Detection Models: Identify at-risk individuals before severe deficiency develops Feature Importance Analysis: Understand which factors most strongly predict nutritional diseases Symptom-Disease Correlation: Map symptom patterns to specific deficiency types Dietary Risk Assessment: Evaluate how diet types correlate with deficiency risks Public Health Modeling: Identify vulnerable populations (vegans, low-income, low sun exposure) Clinical Decision Support: Assist healthcare providers in differential diagnosis Classification Tasks:
Multiclass classification (6 disease categories) Binary classification (Healthy vs. Diseased) Multi-label classification (multiple simultaneous deficiencies) Individual symptom prediction
Recommended Algorithms:
Random Forest Classifier XGBoost / LightGBM Neural Networks Support Vector Machines Logistic Regression (baseline)
Evaluation Metrics:
Accuracy F1-Score (macro/weighted) ROC-AUC Confusion Matrix Analysis Precision-Recall for each disease class
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Additional Information
| Field | Value |
|---|---|
| Data last updated | March 5, 2026 |
| Metadata last updated | March 5, 2026 |
| Created | March 5, 2026 |
| Format | ZIP |
| License | No License Provided |
| Datastore active | False |
| Has views | False |
| Id | 2fe06dbe-403a-434f-a232-b8ca7a6ff264 |
| Mimetype | application/zip |
| Package id | 4399832a-2b64-40a6-8760-6bc54c46a7b8 |
| Position | 0 |
| Size | 165.9 KiB |
| State | active |
| Url type | upload |