fairhealth 0.1.1

Trustworthy Healthcare AI: federated learning, fairness auditing, and explainability for clinical settings

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.

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Install

pip install fairhealth

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Modules and Results

Module	What	Paper	Key Result
fairhealth.fairness	Demographic parity, equalized odds, disparate impact	MobiHealth 2026	DI: 0.23 → 0.71
fairhealth.explain	SHAP wrappers + Fuzzy-XGBoost hybrid	ICAIHE 2026, Waseda	88.67% acc, 71.4% clinician preference
fairhealth.federated	FedAvg + CKKS HE + differential privacy	MedHE, CIBB 2026	macro-F1=0.950, 97.5% comm reduction
fairhealth.lowresource	Dengue triage, multilingual, low-bandwidth	DASGRI 2026, Springer	F1=0.802, 75% satisfaction
fairhealth.equity	Fairness-aware flood aid allocation	CCAI 2026, IEEE	SPD↓41.6%, R²=0.784

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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%

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Research Papers

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

Paper	Venue	Preprint	Status
ECG Fairness	MobiHealth 2026 (EAI)	ResearchGate	Accepted
Maternal Health XAI	ICAIHE 2026, Waseda	ResearchSquare	Accepted
MedHE Federated	CIBB 2026	arXiv:2511.09043	Under review
Dengue Triage	DASGRI 2026, Springer LNNS	ResearchGate	Accepted
Flood Aid Equity	CCAI 2026 (IEEE), oral	arXiv:2512.22210	Accepted

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Datasets (All Public — No Hospital Access Required)

Dataset	Domain	Source
PTB-XL (4,367 records)	ECG biosignals	PhysioNet
Maternal Health Risk (1,014)	Risk prediction	UCI ML Repository
UCI Drug Reviews (215K)	NLP / drug effectiveness	UCI ML Repository
Bangladesh Dengue (4,700)	Symptom triage	Kaggle + DGHS
Bangladesh PDNA 2022 (87 upazilas)	Flood equity	Government open data

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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