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Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma

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Predicting Return to Work after Cardiac Rehabilitation using Machine Learning Models.

Choo Jia Yuan¹, Kasturi Dewi Varathan², Anwar Suhaimi³

¹Department of Information Systems, Faculty of Computer Science & Information Technology, Universiti Malaya, Kuala Lumpur, Malaysia.

Journal of Rehabilitation Medicine

|October 28, 2022

Summary

This summary is machine-generated.

Machine learning models can predict return to work after cardiac rehabilitation. The AdaBoost model achieved 92.4% accuracy using the top 20 features, demonstrating its potential for patient outcomes.

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Area of Science:

Cardiology
Data Science
Rehabilitation Medicine

Background:

Cardiac rehabilitation is crucial for patient recovery.
Predicting return to work aids in patient management and resource allocation.

Purpose of the Study:

To evaluate machine learning models for predicting return to work post-cardiac rehabilitation.
To compare model performance using different feature selection methods.

Main Methods:

Eight machine learning models were assessed.
Models utilized full features, logistic regression significant features, or recursive feature extraction features.
Performance was evaluated using area under the curve (AUC).

Main Results:

The AdaBoost model, using the top 20 features, achieved the highest performance.
The AdaBoost model demonstrated a 92.4% AUC.
Feature selection significantly impacted model performance.

Conclusions:

Machine learning models show promise for predicting return to work after cardiac events.
The AdaBoost model with optimized features is a strong candidate for this prediction task.
These findings can inform clinical decision-making and patient support strategies.