danielke32/FL_malaria: FederatedMalaria

Domain:

healthcare

Record type:

softwaredataset

Creator:

Dan

Publisher:

Host:

Federated Learning for Privacy-Preserving Malaria Prediction

https://github.com/danielke…

Multi-Scenario Evaluation Using Ghana DHS Data

https://www.python.org/down… https://pytorch.org/ https://opensource.org/lice…

Overview

https://github.com/danielke…

This repository contains the complete implementation for the MSc thesis: "Federated Learning for Privacy-Preserving Malaria Prediction: Multi-Scenario Evaluation Using Ghana DHS Data".

The project evaluates federated learning algorithms (FedAvg and FedProx) against centralized baselines for malaria prediction in children under 5 years in Ghana, using data from the Demographic and Health Surveys (DHS) and Malaria Indicator Surveys (MIS).

Key Features

https://github.com/danielke…

Privacy-preserving: Federated learning enables collaborative model training without sharing raw patient data
Multi-scenario evaluation: IID, Non-IID (regional heterogeneity), and data quality variation scenarios
Comprehensive comparison: FedAvg, FedProx vs. centralized Logistic Regression and Random Forest
Reproducible research: Fixed seeds, documented preprocessing, and complete pipeline automation
Publication-ready outputs: Statistical analysis, figures, and tables generation

Repository Structure

https://github.com/danielke…

FL_malaria/
├── data/
│   ├── raw/                    # DHS/MIS Stata files (not included — see Data Access)
│   ├── merged/                 # Merged survey data
│   ├── cleaned/                # Preprocessed datasets
│   │   ├── train_raw.csv          # Raw training data for FL scenarios
│   │   ├── train_centralized.csv  # SMOTE-augmented centralized training
│   │   ├── val_set.csv            # Validation set (real data)
│   │   └── test_set.csv           # Test set (real data)
│   └── fl_scenarios/           # Federated learning client data
│       ├── s1_iid/             # Scenario 1: IID distribution
│       ├── s2_noniid/          # Scenario 2: Regional heterogeneity
│       ├── s3_quality/         # Scenario 3: Data quality variation
│       └── heterogeneity_metrics.json
├── results/
│   ├── centralized_results.json
│   ├── results_baseline.json
│   ├── results_grid_search.json
│   ├── comprehensive_statistical_analysis.json
│   ├── figures/                # Publication-ready figures (PNG + greyscale)
│   └── tables/                 # LaTeX and CSV tables
├── logs/                       # Pipeline execution logs
├── models/                     # Saved model checkpoints
├── validation/                 # Preprocessing validation reports
│
├── src/
│   ├── data/
│   │   ├── data_extraction.py      # Stage 1: DHS/MIS data extraction
│   │   ├── data_preprocessing.py   # Stage 2: MICE imputation & feature engineering
│   │   └── create_fl_scenarios.py  # Stage 3: FL scenario creation
│   ├── training/
│   │   ├── train_centralized.py    # Stage 4: Centralized baseline training
│   │   └── train_federated.py      # Stage 5: Federated model training
│   ├── evaluation/
│   │   ├── final_analysis.py       # Stage 6: Statistical analysis & visualization
│   │   └── sensitivity_analysis.py # Robustness check: native vs. full feature set
│   └── pipeline/
│       └── run_complete_pipeline.py  # Orchestrates the complete workflow
│
├── requirements.txt
└── README.md

Installation

https://github.com/danielke…

Prerequisites

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Python 3.10 or higher
pip package manager
(Optional) CUDA-compatible GPU for faster training

Setup

https://github.com/danielke…

Clone the repository
```
git clone github.com
cd FL_malaria
```

Create a virtual environment

python -m venv federated_ml

# Linux/macOS
source federated_ml/bin/activate

# Windows
federated_ml\Scripts\activate

Install dependencies
```
pip install -r requirements.txt
```

Requirements

https://github.com/danielke…

pandas>=1.5.0
numpy>=1.23.0
scipy>=1.9.0
scikit-learn>=1.1.0
imbalanced-learn>=0.10.0
torch>=2.0.0
statsmodels>=0.13.0
matplotlib>=3.6.0
seaborn>=0.12.0
Pillow>=9.0.0

Data Access

https://github.com/danielke…

DHS/MIS Survey Data

https://github.com/danielke…

This project uses Ghana Demographic and Health Survey (DHS) and Malaria Indicator Survey (MIS) data, which requires registration to access:

Register at DHS Program
Request access to the Ghana datasets listed below
Place files in data/raw/

File Naming Convention

https://github.com/danielke…

Survey	PR file (household)	KR file (children)
MIS 2016	GHPR7BFL.DTA	GHKR7BFL.DTA
MIS 2019	GHPR82FL.DTA	GHKR82FL.DTA
DHS 2022	GHPR8CFL.DTA	GHKR8CFL.DTA

Dataset Characteristics

https://github.com/danielke…

Survey	Children (6–59 mo)	RDT positive	Urban	Rainy season
MIS 2016	2,892	27.8%	42.1%	58.2%
MIS 2019	3,245	21.4%	44.3%	61.4%
DHS 2022	4,150	16.9%	46.8%	55.7%
Combined	10,287	21.2%	44.6%	58.1%

Usage

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Quick Start: Run Complete Pipeline

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# Full pipeline — baseline experiment (default)
python src/pipeline/run_complete_pipeline.py

# With grid search hyperparameter tuning
python src/pipeline/run_complete_pipeline.py --experiment grid_search

# Ablation study (μ values only)
python src/pipeline/run_complete_pipeline.py --experiment ablation

# Skip Stage 1 if data is already extracted
python src/pipeline/run_complete_pipeline.py --skip-extraction

# Run specific stages (e.g. training + analysis only)
python src/pipeline/run_complete_pipeline.py --stages 4,5,6

Step-by-Step Execution

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Stage 1: Data Extraction

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python src/data/data_extraction.py

Extracts and merges Ghana DHS/MIS malaria survey data from Stata files using pandas.read_stata.

Outputs:

data/merged/ghana_malaria_merged.csv
validation/yearly_weighted_prevalence.csv
validation/regional_weighted_prevalence.csv

Stage 2: Data Preprocessing

https://github.com/danielke…

python src/data/data_preprocessing.py

Applies MICE imputation, simulates clinical symptoms from the literature, and creates stratified train/val/test splits.

Key steps:

Multiple Imputation by Chained Equations (MICE) with 5 imputations, averaged
Literature-based symptom simulation (chills, sweating, headache, etc.)
Stratified 60/20/20 split (train / val / test)
SMOTE applied to the centralized training set only: 7,200 total samples at 35% positive class target

Outputs:

data/cleaned/train_raw.csv — raw training split for FL scenario creation
data/cleaned/train_centralized.csv — SMOTE-augmented training set (7,200 samples)
data/cleaned/val_set.csv — validation set (real data, no oversampling)
data/cleaned/test_set.csv — test set (real data, no oversampling)

Stage 3: FL Scenario Creation

https://github.com/danielke…

python src/data/create_fl_scenarios.py

Creates three federated learning scenarios across 5 clients (mapped to Ghana regions).

Scenario	Description	Missingness
S1 (IID)	Random equal split across clients	None
S2 (Non-IID)	Regional assignment — unequal sizes and varying prevalence	None
S3 (Quality)	Same as S2 with injected missing values per client	5–20% per client

S3 missing rates by client: Greater Accra 5%, Ashanti 10%, Northern 20%, Upper East 15%, Western 12%.

Outputs:

data/fl_scenarios/s1_iid/client_{0-4}.csv
data/fl_scenarios/s2_noniid/client_{0-4}.csv
data/fl_scenarios/s3_quality/client_{0-4}.csv
data/fl_scenarios/heterogeneity_metrics.json

Stage 4: Centralized Training

https://github.com/danielke…

python src/training/train_centralized.py

Trains baseline models using GridSearchCV with 5-fold stratified cross-validation.

Models:

Logistic Regression (class-balanced)
Random Forest (class-balanced)

Primary metric: AUC-PR (Average Precision) — appropriate for the class-imbalanced setting.

Outputs:

results/centralized_results.json

Stage 5: Federated Training

https://github.com/danielke…

# Baseline experiment (default)
python src/training/train_federated.py --experiment baseline

# Full grid search
python src/training/train_federated.py --experiment grid_search

# Ablation study (μ values only)
python src/training/train_federated.py --experiment ablation

Algorithms:

FedAvg: standard federated averaging (μ = 0)
FedProx: proximal-term regularization (μ ∈ {0.01, 0.1, 0.5, 1.0})

Configuration:

10 communication rounds
5 clients per scenario
Batch size: 32
Local SMOTE balancing: 35% positive target
Decision threshold: 0.35

Outputs:

results/results_baseline.json
results/results_grid_search.json (grid search only)
results/results_ablation.json (ablation only)

Stage 6: Final Analysis

https://github.com/danielke…

python src/evaluation/final_analysis.py

Generates comprehensive statistical analysis and publication-ready visualizations.

Figures produced (PNG + greyscale copy for print):

figure_1_performance_comparison
figure_2_roc_pr_curves
figure_3_metric_distributions
figure_4_confusion_matrices
figure_5_convergence_analysis
figure_6_ablation_study
figure_7_statistical_summary
figure_8_radar_comparison
figure_9_error_analysis

Tables produced (CSV + LaTeX):

table_1_performance_metrics
table_2_statistical_tests
table_3_centralized_comparison
table_4_ablation_results
table_5_comprehensive_stats

Outputs:

results/comprehensive_statistical_analysis.json
results/figures/ — all figures at 300 DPI
results/tables/ — CSV and .tex files

Sensitivity Analysis (optional)

https://github.com/danielke…

python src/evaluation/sensitivity_analysis.py

Robustness check that re-runs all three FL scenarios and the centralized baseline using only the 6 native DHS features (no synthetically simulated symptoms), then compares AUC-PR and AUC-ROC against the full 12-feature results. A small performance drop confirms that synthetic features do not carry inflated outcome signal.

Native features (6): fever, diarrhea, bednet_use, recent_travel, season, age_group
Synthetic features (6): chills, sweating, headache, bodyaches, nausea_vomiting, appetite_loss

Output: results/sensitivity_analysis.json

Experiment Configurations

https://github.com/danielke…

Baseline

https://github.com/danielke…

mu_values      = [0.0, 0.1]
learning_rates = [0.05]
local_epochs   = [10]
n_seeds        = 10

Grid Search

https://github.com/danielke…

mu_values      = [0.0, 0.01, 0.1, 0.5, 1.0]
learning_rates = [0.01, 0.05]
local_epochs   = [5, 10, 15]
n_seeds        = 10

Ablation Study

https://github.com/danielke…

mu_values      = [0.0, 0.01, 0.1, 0.5, 1.0]
learning_rates = [0.05]
local_epochs   = [10]
n_seeds        = 10

Features

https://github.com/danielke…

The model uses 12 features for malaria prediction:

Feature	Type	Description
`fever`	Binary	Recent fever (from DHS)
`diarrhea`	Binary	Recent diarrhea (from DHS)
`chills`	Binary	Simulated based on literature
`sweating`	Binary	Simulated based on literature
`headache`	Ordinal (0–3)	Simulated severity scale
`bodyaches`	Ordinal (0–3)	Simulated severity scale
`nausea_vomiting`	Binary	Simulated based on literature
`appetite_loss`	Binary	Simulated based on literature
`bednet_use`	Binary	ITN/LLIN usage
`recent_travel`	Binary	Travel to endemic areas
`season`	Binary	Rainy (1) vs. dry (0)
`age_group`	Ordinal (0–5)	Age category (<6 to <60 months)

Reproducibility

https://github.com/danielke…

Fixed Random Seeds

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All experiments use seed 42 with comprehensive seeding:

def set_all_seeds(seed: int):
    random.seed(seed)
    np.random.seed(seed)
    torch.manual_seed(seed)
    torch.cuda.manual_seed(seed)
    torch.cuda.manual_seed_all(seed)
    torch.backends.cudnn.deterministic = True
    torch.backends.cudnn.benchmark = False
    os.environ['PYTHONHASHSEED'] = str(seed)

Multiple Seeds for Statistical Validity

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10 independent runs per configuration (seeds 42–51)
Results reported as mean ± standard deviation
Statistical tests: paired t-tests and Tukey HSD for significance

Results Summary

https://github.com/danielke…

Primary Findings

https://github.com/danielke…

FedProx outperforms FedAvg in heterogeneous scenarios (S2, S3)
Optimal μ = 0.5 for FedProx in non-IID settings
Federated models achieve comparable performance to centralized baselines
AUC-PR is the most appropriate metric for the class-imbalanced malaria prediction task

Key Metrics

https://github.com/danielke…

Model	Scenario	AUC-PR	AUC-ROC	F1 Score
FedAvg	S1 (IID)	—	—	—
FedProx	S1 (IID)	—	—	—
FedAvg	S2 (Non-IID)	—	—	—
FedProx	S2 (Non-IID)	—	—	—
FedAvg	S3 (Data Quality)	—	—	—
FedProx	S3 (Data Quality)	—	—	—
Centralized LR	—	—	—	—
Centralized RF	—	—	—	—

(Populate after running experiments)

Citation

https://github.com/danielke…

If you use this code in your research, please cite:

@mastersthesis{kovor2025federated,
  title={Federated Learning for Privacy-Preserving Malaria Prediction:
         Multi-Scenario Evaluation Using Ghana DHS Data},
  author={Kovor, Kwasi Daniel},
  year={2025},
  school={Kwame Nkrumah University of Science and Technology},
  type={MSc Thesis}
}

Acknowledgments

https://github.com/danielke…

DHS Program for providing access to Ghana survey data
WHO and malaria literature for clinical symptom parameters

License

https://github.com/danielke…

This project is licensed under the MIT License — see the LICENSE file for details.

Contact

https://github.com/danielke…

Author: Daniel Kwasi Kovor
Email: dkkovor@st.knust.edu.gh / danielke32@gmail.com
Institution: Kwame Nkrumah University of Science and Technology

danielke32/FL_malaria: FederatedMalaria

Domain:

Record type:

Federated Learning for Privacy-Preserving Malaria Prediction

Overview

Key Features

Repository Structure

Installation

Prerequisites

Setup

Requirements

Data Access

DHS/MIS Survey Data

File Naming Convention

Dataset Characteristics

Usage

Quick Start: Run Complete Pipeline

Step-by-Step Execution

Stage 1: Data Extraction

Stage 2: Data Preprocessing

Stage 3: FL Scenario Creation

Stage 4: Centralized Training

Stage 5: Federated Training

Stage 6: Final Analysis

Sensitivity Analysis (optional)

Experiment Configurations

Baseline

Grid Search

Ablation Study

Features

Reproducibility

Fixed Random Seeds

Multiple Seeds for Statistical Validity

Results Summary

Primary Findings

Key Metrics

Citation

Acknowledgments

License

Contact

Visit

Languages

Licenses