BhinnekaAI/CLB-Patient-Screening

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