Model evaluation K folds and Leave one out
Introduction to Machine Learning in Healthcare
- Focus on practical application of machine learning in healthcare.
- Discussion on training machine learning models specifically for healthcare problems.
Knowledge Discovery Process
- Raw data needs to be filtered to become usable.
- Classification is essential to identify relevant patterns.
- Example of vast datasets (like smartwatch readings) often being mostly irrelevant (the paradox of dimensionality).
- Need to scale down data to extract useful attributes and identify significant patterns.
Types of Machine Learning Tasks
- Clustering: Identifying groups within data based on shared relevancy.
- Example: clustering patients based on symptoms.
- Predictive Tasks: Feeding data to a network to receive predictions (like health conditions).
- Real-world example of smartphones identifying dog breeds through images.
- Naive Bayes, decision trees, and artificial neural networks are mentioned.
- Focus on decision trees for simplicity and explainability.
Classification Problems in Healthcare
Example dataset from students with attributes: activity, sleep time, blood pressure, heart rate, heart rate variability, stress levels (binary).
Predictor attributes are activity, sleep time, etc.; the class attribute is stress level (to be predicted).
Explanation of how to classify new data points based on learned patterns from the dataset.
Decision Tree Model
- A visual representation helps to understand predictions and decision paths clearly.
- Explains stepwise how to follow attributes (e.g., sleeping time, heart rate variability) to reach predictions.
- Decision trees allow explainability, which is crucial in healthcare for trust and accountability.
- Comparison to neural networks which are termed "black box" models due to opacity in decision-making.
Evaluating Machine Learning Models
- Introduces cross-validation as a method for ensuring model reliability.
- K-Fold Cross-Validation: Dividing data into k chunks, training on k-1 chunks and testing on the remaining one.
- Ensures that the model is robust and prevents overfitting.
- Importance of stratification in maintaining a representative mix of data in training and testing sets.
Challenges in Model Evaluation
- Overfitting can occur in K-fold if training on seen data leads to wrong conclusions when applied to unseen data.
- The necessity for models to generalize well to new data is crucial in healthcare contexts, where unseen patient data is the norm.
Leave-One-Out Cross-Validation
- An alternative that tests each participant's data separately to evaluate the model's predictive ability on entirely new data.
- More reflective of real-world applications, though potentially computationally expensive.
Final Thoughts
- Emphasizes the importance of combining good modeling with thorough evaluation practices.
- Challenges of machine learning in healthcare revolve around the requirement for interpretability and reliability due to high-stakes consequences.
- Encourages looking beyond standard methodologies when specific application contexts (like healthcare) demand it.
Next Steps
- Upcoming lectures will cover practical eHealth systems and their real-world implementations.
- Questions welcomed.