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.