tarekmasryo/blood-donation-registry-dataset

🩸 Blood Donation Registry — Synthetic Donors, Prevalence & Compatibility

A synthetic blood donation operations dataset for analysis and decision-focused modeling: eligibility/deferrals, donation history, rare blood types, country-level prevalence, and RBC transfusion compatibility (ABO/Rh).

✅ Synthetic data (safe for experimentation/teaching).
⚠️ Not clinical/medical ground truth and not intended for real-world medical decision-making.

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Dataset summary

This dataset is designed to support portfolio-grade notebooks and practical workflows:

• exploratory analysis (EDA) and segmentation
• propensity / likelihood modeling and calibration
• threshold selection under capacity/budget constraints
• rare-blood availability analysis using prevalence + compatibility rules

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Data files

1) blood_donation_registry_ml_ready.csv (30,000 rows × 27 columns)

Donor-level snapshot with a fixed reference date:

• as_of_date = 2024-12-31

Key groups

• Identity & geography: donor_id (unique), country_code, region
• Profile: age, sex (M/F), bmi, smoker (0/1), chronic_condition_flag (0/1)
• Eligibility & deferrals: eligibility_status, eligible_to_donate (0/1), deferral_reason
• Donation behavior/history: donation_count_last_12m, lifetime_donation_count, first_donation_year, years_since_first_donation, last_donation_date, recency_days, is_regular_donor (0/1), donor_age_at_first_donation, preferred_site
• Blood context: blood_type (8 types), is_rare_type (0/1), blood_type_country_prevalence
• Engineered score (optional): donation_propensity_score

Outcome columns

• donated_next_6m (0/1)
• next_6m_donation_count (0–3)

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2) blood_population_distribution.csv (39 rows × 12 columns)

Country-level population + blood type prevalence:

• country_code, region, population_size
• proportions: p_o_pos, p_o_neg, p_a_pos, p_a_neg, p_b_pos, p_b_neg, p_ab_pos, p_ab_neg
• rh_negative_rate

Integrity note: blood type proportions sum to 1.0 per country.

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3) blood_compatibility_lookup.csv (64 rows × 4 columns)

RBC transfusion compatibility matrix:

• donor_blood_type, recipient_blood_type
• compatible_for_rbc_transfusion (0/1)
• compatibility_level: ideal | acceptable | incompatible

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4) data_dictionary.csv

Column-level documentation:

• file, column_name, type, description, allowed_values_or_range, missing_values

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Relationships (how tables connect)

• blood_donation_registry_ml_ready.csv.country_code ↔ blood_population_distribution.csv.country_code

• blood_donation_registry_ml_ready.csv.blood_type_country_prevalence is derived from the matching country prevalence table.

• blood_compatibility_lookup.csv provides rule-based compatibility for blood type pairs.

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Recommended tasks

1) Donation likelihood (binary classification)

• Outcome: donated_next_6m
• Suggested evaluation: ROC-AUC, PR-AUC, F1, plus calibration (reliability curve / Brier score)

2) Donation frequency (count prediction)

• Outcome: next_6m_donation_count
• Suggested evaluation: MAE, RMSE (optional Poisson/Ordinal baselines)

3) Decision policy under constraints

Turn probabilities into an outreach policy:

• choose an operating threshold given capacity/budget
• compare FP/FN tradeoffs
• validate stability across segments (country/region, rare types, eligibility)

4) Rare blood operations analytics

• analyze is_rare_type by country prevalence
• explore compatibility-aware matching using the lookup matrix

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Modeling notes (avoid leakage / shortcuts)

This dataset intentionally includes engineered and overlapping fields for different notebook styles.

• donated_next_6m is derived from next_6m_donation_count → use one outcome as the target.
• eligible_to_donate overlaps with eligibility_status → keep one for simpler baselines.
• eligible_to_donate == 0 implies donated_next_6m == 0 → for behavior modeling, consider restricting training to eligible_to_donate == 1.
• donation_propensity_score is an engineered signal; exclude it for “feature-only” benchmark baselines.

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Data quality expectations

• donor_id is unique (no duplicate donors)
• no duplicate rows
• snapshot consistency (as_of_date fixed)
• recency_days aligns with as_of_date - last_donation_date
• country codes match the prevalence table
• compatibility lookup covers all 8×8 donor/recipient pairs

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Synthetic data generation (high-level)

Records are simulated to reflect realistic constraints and patterns:

• eligibility rules and deferral reasons (age/BMI/health flags)
• donation history distributions and recency behavior
• country-level blood type prevalence used to derive per-donor prevalence context
• ABO/Rh compatibility rules encoded in the lookup table

Synthetic distributions may not match any specific real-world population.

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Limitations

• Snapshot-style dataset (not a full longitudinal event log beyond history fields)
• Synthetic distributions may differ from real operational settings
• Engineered signals (e.g., donation_propensity_score) can act as shortcut features if used without care

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Quick start

import pandas as pd

donors = pd.read_csv("blood_donation_registry_ml_ready.csv")
pop    = pd.read_csv("blood_population_distribution.csv")
compat = pd.read_csv("blood_compatibility_lookup.csv")

# Example join: add country population
donors = donors.merge(pop[["country_code", "population_size"]], on="country_code", how="left")
print(donors.shape)

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License

CC BY 4.0 — attribution required.

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Author

Tarek Masryo