Agniv1/obesity-disease-transmission-ml

Effect of Obesity in Disease Transmission Dynamics using Machine Learning

This repository contains a comprehensive machine learning pipeline that investigates how obesity affects both individual disease risk and population-level disease transmission dynamics.

Overview

The project addresses the research question: "What is the effect of obesity in disease transmission dynamics?" using machine learning approaches across two complementary domains:

1. Individual Risk Prediction: Predicting stroke and heart disease from clinical features including BMI/obesity status
2. Population Transmission Dynamics: Simulating and forecasting epidemic spread with obesity as a comorbidity risk modifier

Datasets

• Stroke Prediction Dataset (Nnaodeh/Stroke_Prediction_Dataset)
  • 5,110 patient records with 11 clinical features
  • BMI used as obesity proxy (BMI ≥ 30 = obese)
  • Target variables: stroke and heart_disease

Methods

Individual Disease Risk Models

• Logistic Regression (best for stroke: AUC = 0.839)
• Random Forest
• Gradient Boosting
• XGBoost
• Multi-Layer Perceptron (Neural Network)

Epidemic Simulation

• Agent-based SIR model (Susceptible-Infected-Recovered)
• Population: 10,000 individuals
• Obesity susceptibility multiplier: 2.0x
• Base infection rate: 0.03 per contact
• Recovery rate: 0.08 (~12 day infectious period)

Epidemic Forecasting Models

• Ridge Regression
• Random Forest Regressor
• Gradient Boosting Regressor
• XGBoost Regressor (best: MAE = 3.78 for total infections)

Key Findings

Obesity Effect on Individual Disease Risk

Disease	Obese Rate	Non-obese Rate	Relative Risk
Stroke	5.10%	4.73%	1.08x
Heart Disease	6.25%	4.89%	1.28x

Obesity Effect on Epidemic Transmission

• Obese attack rate: 96.49%
• Non-obese attack rate: 78.97%
• Relative risk: 1.22x
• Peak infected day: Day 44 (3,816 simultaneous infections)

Feature Importance (Stroke Prediction)

Top predictive features:

1. Age (most important)
2. Average glucose level
3. BMI / Obesity status

Files

• obesity_disease_transmission.py — Complete pipeline script
• report.json — Detailed metrics and results
• results/*.png — Visualizations

Reproduction

pip install scikit-learn xgboost datasets matplotlib pandas numpy
python obesity_disease_transmission.py

Citation

• Stroke Prediction Dataset: Nnaodeh/Stroke_Prediction_Dataset
• Christakis & Fowler (2007): "The Spread of Obesity in a Large Social Network over 32 Years"

Generated by ML Intern

This model repository was generated by ML Intern, an agent for machine learning research and development on the Hugging Face Hub.

• Try ML Intern: https://smolagents-ml-intern.hf.space
• Source code: https://github.com/huggingface/ml-intern

Usage

from transformers import AutoModelForCausalLM, AutoTokenizer

model_id = 'Agniv1/obesity-disease-transmission-ml'
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(model_id)

For non-causal architectures, replace AutoModelForCausalLM with the appropriate AutoModel class.