Developing predictive models involves several steps:
1. Data Collection: Gathering relevant data from multiple sources. 2. Data Preprocessing: Cleaning and organizing data to ensure accuracy and consistency. 3. Feature Selection: Identifying important variables that influence disease outcomes. 4. Model Training: Using algorithms to train models on historical data. 5. Model Validation: Testing the model on new data to assess its performance. 6. Deployment: Implementing the model in real-world settings for continuous monitoring and prediction.
