Synopsis
Contents
Preface
Section I Cluster and Classification Models
1 Hierarchical Clustering and K-means Clustering to Identify Subgroups in Surveys (50 Patients)2 Density-based Clustering to Identify Outlier Groups in Otherwise Homogeneous Data (50 Patients)
3 Two Step Clustering to Identify Subgroups and Predict Subgroup Memberships in Individual Future Patients (120 Patients)
4 Nearest Neighbors for Classifying New Medicines (2 New and 25 Old Opioids)
5 Predicting High-Risk-Bin Memberships (1445 Families)
6 Predicting Outlier Memberships (2000 Patients)
7 Data Mining for Visualization of Health Processes (150 Patients)
8 Trained Decision Trees for a More Meaningful Accuracy (150 Patients) 9 Typology of Medical Data (51 Patients)
10 Predictions from Nominal Clinical Data (450 Patients)
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