AIMC Topic: Decision Trees

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Hierarchical classification of large-scale patient records for automatic treatment stratification.

IEEE journal of biomedical and health informatics
In this paper, a hierarchical learning algorithm is developed for classifying large-scale patient records, e.g., categorizing large-scale patient records into large numbers of known patient categories (i.e., thousands of known patient categories) for...

Artificial Intelligence Systems as Prognostic and Predictive Tools in Ovarian Cancer.

Annals of surgical oncology
BACKGROUND: The ability to provide accurate prognostic and predictive information to patients is becoming increasingly important as clinicians enter an era of personalized medicine. For a disease as heterogeneous as epithelial ovarian cancer, convent...

Learning machines and sleeping brains: Automatic sleep stage classification using decision-tree multi-class support vector machines.

Journal of neuroscience methods
BACKGROUND: Sleep staging is a critical step in a range of electrophysiological signal processing pipelines used in clinical routine as well as in sleep research. Although the results currently achievable with automatic sleep staging methods are prom...

Stable feature selection for clinical prediction: exploiting ICD tree structure using Tree-Lasso.

Journal of biomedical informatics
Modern healthcare is getting reshaped by growing Electronic Medical Records (EMR). Recently, these records have been shown of great value towards building clinical prediction models. In EMR data, patients' diseases and hospital interventions are capt...

The Impact of Oversampling with SMOTE on the Performance of 3 Classifiers in Prediction of Type 2 Diabetes.

Medical decision making : an international journal of the Society for Medical Decision Making
OBJECTIVE: To evaluate the impact of the synthetic minority oversampling technique (SMOTE) on the performance of probabilistic neural network (PNN), naïve Bayes (NB), and decision tree (DT) classifiers for predicting diabetes in a prospective cohort ...

Machine-learning approaches in drug discovery: methods and applications.

Drug discovery today
During the past decade, virtual screening (VS) has evolved from traditional similarity searching, which utilizes single reference compounds, into an advanced application domain for data mining and machine-learning approaches, which require large and ...

Automated structural classification of lipids by machine learning.

Bioinformatics (Oxford, England)
MOTIVATION: Modern lipidomics is largely dependent upon structural ontologies because of the great diversity exhibited in the lipidome, but no automated lipid classification exists to facilitate this partitioning. The size of the putative lipidome fa...

Rule extraction from support vector machines using ensemble learning approach: an application for diagnosis of diabetes.

IEEE journal of biomedical and health informatics
Diabetes mellitus is a chronic disease and a worldwide public health challenge. It has been shown that 50-80% proportion of T2DM is undiagnosed. In this paper, support vector machines are utilized to screen diabetes, and an ensemble learning module i...