C4.5: programs for machine learning
C4.5: programs for machine learning
Feature learning with a genetic algorithm for fluorescence fingerprinting of plant species
Pattern Recognition Letters
Data Mining: Practical Machine Learning Tools and Techniques, Second Edition (Morgan Kaufmann Series in Data Management Systems)
Predicting breast cancer survivability: a comparison of three data mining methods
Artificial Intelligence in Medicine
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Objective: To systematically investigate whether overnight features in transcutaneous carbon dioxide (P"T"c"C"O"""2) measurements can predict metabolic variables in subject with suspected sleep-disordered breathing. Methods: The features extracted from the P"T"c"C"O"""2 signal included the number of abrupt descents per hour and attributes that characterize the recovery after such an event. For each outcome variable, the subgroup of the 108 study subjects with the particular variable present was divided into two representative classes, and the optimal features that can predict the classes were learned. Overfitting was avoided by evaluating the classification algorithms using 10-fold cross-validation. Results: P"T"c"C"O"""2 signal has a key role in determining the classes of high-density lipoprotein cholesterol and thyroid-stimulating hormone concentrations, and it improves the classification accuracy of glycosylated hemoglobin A1c and fasting plasma glucose values. Conclusions: The features learned from the P"T"c"C"O"""2 signal reflected the state of the selected metabolic variables in a subtle, but systematic, way. These findings provide a step towards understanding how metabolic disturbances are connected to carbon dioxide exchange during sleep.