Academy

Machine Learning in Genetic Risk Prediction

Introduction: The application of machine learning (ML) to genetic data is transforming our understanding of disease susceptibility. In occupations with high noise exposure, identifying workers at risk of noise-induced hearing loss (NIHL) requires analyzing complex genetic variations known as single nucleotide polymorphisms (SNPs). This course explains how ML methods handle high-dimensional SNP datasets to predict health outcomes.

Key Concepts

• Single Nucleotide Polymorphism (SNP): A SNP is a single-base difference in DNA sequence among individuals. SNPs in specific genes can influence how the inner ear responds to noise and oxidative stress.
• High-Dimensional Data: Genotyping hundreds of SNPs creates a dataset with many features per individual. Traditional statistical models struggle with such data due to multicollinearity and nonlinear interactions.
• Machine Learning Models: ML algorithms like Random Forests (RF), Decision Trees (DT), and neural networks (PNN, GRNN) can learn patterns from complex datasets. They automatically capture interactions among SNPs and environmental factors without predefined assumptions.

Popular ML Approaches

1. Random Forest and GA-RF: Random Forest builds an ensemble of decision trees on bootstrap samples. GA-RF integrates a genetic algorithm to optimize parameters (e.g., tree depth, feature subsets), improving predictive accuracy on SNP data.
2. Neural Networks (PNN & GRNN): Probabilistic Neural Networks classify samples based on estimated probability density functions for each class. Generalized Regression Neural Networks estimate continuous outputs via kernel smoothing. Both models handle nonlinearity and high feature counts.
3. Gradient Boosting (XGBoost, GBDT): These methods iteratively fit tree-based learners to residual errors, building strong predictors. Hyperparameter tuning is critical to avoid overfitting.

Model Evaluation Metrics

• Accuracy: Percentage of correctly predicted cases and controls.
• Recall (Sensitivity): Ability to identify actual hearing loss cases.
• Precision: Proportion of predicted cases that are true cases.
• F-score: Harmonic mean of recall and precision.
• AUC (ROC): Measures discriminative power across all thresholds.
• Nagelkerke R²: Pseudo R² for binary outcomes, indicating model fit.

Applications in Occupational Health

By applying ML to SNP panels and exposure data, occupational health practitioners can:

• Screen workers for genetic susceptibility to NIHL.
• Implement targeted preventive measures (e.g., personalized hearing protection).
• Monitor high-risk individuals more closely over time.

Understanding ML fundamentals and careful model evaluation ensures reliable disease risk predictions from genetic data, driving personalized medicine in workplace safety.