FairHealth

Trustworthy Healthcare AI — built from peer-reviewed research.

FairHealth is an open-source Python library for building fair, explainable, and privacy-preserving machine learning models for healthcare.

Built by Farjana Yesmin from 5 accepted research papers.

Install

pip install fairhealth

Modules and Results

Quick Example

import fairhealth as fh
import numpy as np

# Fairness audit
from fairhealth.fairness.metrics import demographic_parity_diff
dpd = demographic_parity_diff(
    y_pred    = np.array([1, 0, 1, 0, 1, 0]),
    sensitive = np.array([0, 0, 0, 1, 1, 1])
)
print(f"DPD: {dpd:.4f}")   # → 0.3333

# Dengue triage — English + Bangla
from fairhealth.lowresource.triage import assess_dengue_risk
result = assess_dengue_risk(age=8, gender="male",
                             area_type="urban", district="Dhaka",
                             language="bangla")
print(result["recommendation"])   # বাংলা output

# Flood aid equity
from fairhealth.equity.flood_aid import generate_priority_ranking
rankings = generate_priority_ranking(verbose=False)
print(f"Top priority: {rankings[0]['district']}")  # → Sunamganj

# Federated privacy
from fairhealth.federated.privacy import sparsify
_, rate = sparsify(np.random.randn(1000), sparsity=0.975)
print(f"Communication reduced: {rate:.1%}")  # → 97.5%

Research Papers

All papers are accepted. Preprint links below; final proceedings forthcoming.

Datasets (All Public — No Hospital Access Required)

Cite

@article{yesmin2026fairhealth,
  author  = {Yesmin, Farjana},
  title   = {FairHealth: An Open-Source Python Library for
             Trustworthy Healthcare AI in Low-Resource Settings},
  journal = {arXiv preprint arXiv:2605.08198},
  year    = {2026},
  url     = {https://arxiv.org/abs/2605.08198}
}

@software{fairhealth2026,
  author = {Yesmin, Farjana},
  title  = {FairHealth: Trustworthy Healthcare AI},
  year   = {2026},
  url    = {https://github.com/Farjana-Yesmin/fairhealth}
}

Author: Farjana Yesmin · farjana-yesmin.github.io · MIT License