📄️ 8.1 ML based Logistic Regression in Healthcare
This section introduces machine learning-based logistic regression in the healthcare context, emphasizing its role in binary classification tasks. It covers the process of model training and evaluation, hyperparameter tuning, interpretability, and offers a case study on predicting patient readmission. The section also discusses the importance of model deployment while addressing privacy and security concerns in healthcare settings.
📄️ 8.2 Decision Trees for Clinical Decision Support
This section introduces decision trees in the context of clinical decision support. It covers the structure of decision trees, model training, interpretability, handling missing data and categorical features, and the use of decision trees for predicting disease risk. The section also addresses model evaluation, overfitting, and introduces ensemble methods like random forests. It concludes by highlighting the role of decision trees in clinical decision support systems.
📄️ 8.3 Naive Bayes for Diagnostic Tests
This section introduces the Naive Bayes algorithm in the context of diagnostic testing and medical decision-making. It covers the basics of Bayesian probability, the independence assumption, application in diagnostic testing, model training, types of Naive Bayes classifiers, and a case study for disease diagnosis. The section also addresses handling continuous and categorical data, strengths and limitations of Naive Bayes, clinical integration, and concludes by emphasizing its role in clinical decision support systems.
📄️ 8.4 Evaluating Classifier Performance in Healthcare
This section discusses the importance of evaluating classifier performance in healthcare machine learning. It covers the confusion matrix, accuracy, precision, recall, F1-score, ROC curve, PR curve, and how these metrics are essential for assessing model effectiveness. A case study demonstrates the application of classifier evaluation in diagnosing diseases. The section also highlights the significance of cross-validation, the balance between overfitting and underfitting, and choosing the right metric based on the healthcare application's goals. The conclusion emphasizes the necessity of reliable evaluation for trustworthy machine learning models in healthcare.
📄️ Resources
- Scikit-learn for Predictions