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Related Experiment Video

Updated: Jul 28, 2025

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances

Published on: October 11, 2018

Deep learning based decision tree ensembles for incomplete medical datasets.

Chien-Hung Chiu¹, Shih-Wen Ke², Chih-Fong Tsai²

¹Division of Thoracic Surgery, Chang Gung Memorial Hospital at Linkou, Taoyuan, Taiwan.

Technology and Health Care : Official Journal of the European Society for Engineering and Medicine

|May 30, 2023

Summary

This summary is machine-generated.

This study introduces Deep Learning-based Decision Tree Ensembles (DLDTE) to effectively handle incomplete medical datasets. DLDTE achieved superior classification accuracy compared to existing methods for missing data.

Keywords:

Data science classifier ensembles decision trees deep learning missing value imputation

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Area of Science:

Machine Learning
Data Science
Medical Informatics

Background:

Incomplete datasets with missing attribute values are common in data analysis.
Existing solutions involve either imputation or direct handling of missing data, often using decision trees.

Purpose of the Study:

To introduce Deep Learning-based Decision Tree Ensembles (DLDTE) for analyzing incomplete datasets.
To leverage deep learning strategies for improved decision tree ensemble performance.

Main Methods:

DLDTE utilizes bounding box and sliding window strategies from deep learning.
The approach divides incomplete datasets into subsets for decision tree learning.
Performance was evaluated on two medical datasets with 10%-50% missing rates.

Main Results:

DLDTE demonstrated the highest classification accuracy.
It outperformed baseline decision trees, mean imputation, k-nearest neighbor imputation, and case deletion methods.

Conclusions:

The proposed DLDTE method is effective for handling incomplete medical datasets.
Results confirm DLDTE's efficacy across various missing data rates.