Ensemble Feature Selection and Harris Hawks Optimization for Explainable Mental Health Risk Prediction in Female Sex Workers

1. 1Apply ensemble feature selection to identify relevant factors contributing to mental health risks in female sex workers
2. 2Use Harris Hawks Optimization to optimize the feature selection process and improve model performance
3. 3Implement a hybrid predictive model that combines multiple machine learning algorithms to capture complex risk patterns
4. 4Evaluate the model's performance using metrics such as accuracy, precision, and recall
5. 5Interpret the results to identify key factors associated with mental health risks in this population

Data scientists and machine learning engineers working on healthcare projects can benefit from this approach to improve the accuracy and explainability of their models, while researchers in social sciences can apply these methods to better understand mental health risks in vulnerable populations

💡 Ensemble feature selection and Harris Hawks Optimization can improve the accuracy and explainability of machine learning models for mental health risk prediction in marginalized groups

New approach to mental health risk prediction in female sex workers using ensemble feature selection and Harris Hawks Optimization #MachineLearning #MentalHealth