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Multiview Deep Forest for Overall Survival Prediction in Cancer.

Qiucen Li¹, Zedong Du², Zhikui Chen¹

¹Dalian University of Technology, School of Software, China.

Computational and Mathematical Methods in Medicine

|January 30, 2023

Summary

This summary is machine-generated.

This study introduces a novel Multiview Deep Forest (MVDF) model to accurately predict overall survival (OS) in cancer patients. The MVDF method effectively handles complex data and reduces overfitting, improving cancer prognosis prediction.

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Area of Science:

Oncology
Machine Learning
Bioinformatics

Background:

Overall survival (OS) is a critical metric in cancer treatment and prognosis.
Existing machine learning methods struggle with multiview data and overfitting in OS prediction.

Purpose of the Study:

To develop an advanced machine learning model for accurate cancer overall survival prediction.
To address the challenges of multiview data integration and overfitting in survival analysis.

Main Methods:

Proposed a Multiview Deep Forest (MVDF) model integrating features from multiple data views.
Employed integrated learning and multiple kernel learning for feature fusion.
Utilized a gradient boost forest based on information bottleneck theory to minimize redundancy and prevent overfitting.
Implemented a pruning strategy for cascaded forests to mitigate outlier impact.

Main Results:

The MVDF model demonstrated superior performance in predicting overall survival compared to existing methods.
Experiments conducted on clinical and public datasets validated the model's effectiveness.
The proposed methods successfully handled multiview data and reduced overfitting.

Conclusions:

The Multiview Deep Forest (MVDF) model offers a robust solution for cancer overall survival prediction.
MVDF's ability to manage multiview data and prevent overfitting enhances its clinical applicability.
This approach advances the field of machine learning in cancer prognosis.