Predicting mental health issues among the working sector using machine learning involves several steps, including data collection, preprocessing, model selection, training, evaluation, and deployment. Here’s a structured approach to tackle this problem:
1. Data Collection
Surveys and Questionnaires: Collect data through validated mental health surveys like the General Health Questionnaire (GHQ), Patient Health Questionnaire (PHQ), or customized workplace surveys. HR Records: Absenteeism, job satisfaction scores, performance reviews, and other HR metrics. Workplace Metrics: Workload, working hours, job role, and department. Demographics: Age, gender, education level, and tenure. External Factors: Economic indicators, social factors, and personal life events if available.
2. Data Preprocessing
Cleaning: Handle missing values, duplicates, and inconsistencies. Normalization: Scale features to ensure uniformity. Encoding: Convert categorical variables into numerical ones using techniques like one-hot encoding. Feature Engineering: Create new features that might be relevant, such as stress levels based on workload or sentiment analysis from employee feedback.
3. Model Selection
Classification Algorithms: Logistic Regression, Decision Trees, Random Forests, Support Vector Machines (SVM), Gradient Boosting, Neural Networks. Ensemble Methods: Combining multiple models to improve accuracy. Evaluation Metrics: Accuracy, precision, recall, F1-score, ROC-AUC.
4. Model Training
Splitting Data: Divide data into training and test sets (typically 70-30 or 80-20 split). Cross-Validation: Use techniques like k-fold cross-validation for better model validation. Hyperparameter Tuning: Optimize model parameters using grid search or randomized search.
5. Model Evaluation
Confusion Matrix: To understand the true positives, false positives, true negatives, and false negatives. ROC Curve: To evaluate the trade-off between sensitivity and specificity. Model Interpretation: Use tools like SHAP (SHapley Additive exPlanations) values to interpret model predictions.