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Pooling random forest and functional data analysis for biomedical signals supervised classification: Theory and

Fabrizio Maturo¹, Rosanna Verde¹

¹Department of Mathematics and Physics, University of Campania Luigi Vanvitelli, Caserta, Italy.

Statistics in Medicine

|February 20, 2022

Summary

This summary is machine-generated.

This study introduces a novel functional classification method using functional data analysis, classification trees, and random forest to accurately analyze high-dimensional biomedical data. The approach enhances disease prediction, achieving record-breaking accuracy on electrocardiogram (ECG) datasets.

Keywords:

functional between groups variability functional between leaves variability functional classification trees functional data analysis functional random forest

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Area of Science:

Biomedical Engineering
Data Science
Machine Learning

Background:

Modern devices generate vast, high-dimensional biomedical data for health monitoring and disease prediction.
Analyzing this data presents challenges like the complexity-accuracy trade-off and the curse of dimensionality (COD).

Purpose of the Study:

To propose an original supervised classification method for high-dimensional biomedical time-series data.
To develop novel tools for feature extraction, functional classifier training, and rule interpretation.
To improve the accuracy of functional classifiers and overcome COD limitations.

Main Methods:

Joint application of functional data analysis, classification trees, and random forest.
Development of original tools for feature extraction and functional classifier training.
Implementation of interpretative tools for classification rules and leaf assessment.

Main Results:

The proposed method effectively handles high-dimensional biomedical data, including electrocardiogram (ECG) signals.
Interpretative tools provide valuable insights into classification rules.
The functional classifier achieved excellent accuracy, setting a new record on a benchmark ECG dataset.

Conclusions:

The developed functional classification approach offers an effective solution for analyzing complex biomedical data.
This method demonstrates significant potential for identifying and classifying patients at risk.
The interpretative capabilities enhance the clinical utility of the classification model.