| --- |
| license: cc-by-4.0 |
| author : Christopher Alberto Hamonangan |
| --- |
| |
| # Overview |
|
|
| CLB-Patient-Screening is a synthetic benchmark designed for tabular anomaly |
| detection in healthcare, focusing on multi-biomarker systemic drift and early |
| signs of chronic metabolic disorders. |
|
|
| ## Dataset information |
|
|
| | Property | Description | |
| | --------------------------- | ------------------------------------------------- | |
| | Dataset name | CLB-Patient-Screening | |
| | Full name | Clinical Laboratory Biomarker Panel | |
| | Publisher | BhinnekaAI | |
| | Dataset Creator | Christopher Alberto Hamonangan | |
| | Domain | Healthcare and clinical laboratory screening | |
| | Data modality | Structured numerical tabular data | |
| | Feature count | 5 numerical features | |
| | Data type | Synthetic | |
| | Task | Pathological anomaly detection | |
| | Expected anomaly proportion | Approximately 5.0% | |
| | Label convention | `0` = healthy patient; `1` = pathological anomaly | |
|
|
| # Dataset Structure |
|
|
| ## random-seed |
|
|
| - Directory: random-seed |
| - Files: 5 files, 5,000 observations each. |
|
|
| ## seed42 |
|
|
| - Directory: seed42 |
| - Files: 5 files with sizes 100, 1,000, 10,000, 50,000, and 100,000. |
|
|
| # Feature Specification |
|
|
| | Feature | Description | Data Type | |
| | -------------------- | ------------------------------------------- | ---------- | |
| | `blood_pressure` | Systolic blood pressure (mmHg) | Continuous | |
| | `blood_glucose` | Fasting blood glucose level (mg/dL) | Continuous | |
| | `ldl_cholesterol` | Low-density lipoprotein cholesterol (mg/dL) | Continuous | |
| | `white_blood_cells` | White blood cell count ($10^3/\mu L$) | Continuous | |
| | `inflammatory_index` | Composite systemic inflammation score | Continuous | |
|
|
| # Mathematical Formulation |
|
|
| Normal biomarkers are modeled using a Multivariate Gaussian distribution |
| reflecting strict physiological homeostasis: |
|
|
| x_normal ~ N(μ, Σ) |
| |
| where μ = [120, 90, 130, 6.5, 1.8] and Σ represents biological covariance constraints. |
| |
| # Loading the Dataset |
| |
| ```python |
| import pandas as pd |
| |
| df = pd.read_csv("seed42/dataset_10000_rows_seed42.csv") |
| X = df.iloc[:, :-1].values |
| y = df['is_pathological'].values |
| ``` |
