metadata
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
import pandas as pd
df = pd.read_csv("seed42/dataset_10000_rows_seed42.csv")
X = df.iloc[:, :-1].values
y = df['is_pathological'].values