Cancer Prognosis and Diagnosis Methods Based on Ensemble Learning

Abstract

Ensemble methods try to improve performance via integrating different kinds of input data, features or learning algorithms. In addition to other areas, they are finding their applications in cancer prognosis and diagnosis. However, in this area, the research community is lagging behind the technology. A systematic review along with a taxonomy on ensemble methods used in cancer prognosis and diagnosis, can pave the way for the research community to keep pace with the technology and even lead trend. In this paper, we first present an overview on existing relevant surveys, and highlight their shortcomings, which raise the need for a new survey focusing on Ensemble Classifiers (ECs) used for the diagnosis and prognosis of different cancer types. Then we exhaustively review the existing methods, including the traditional ones as well as those based on deep learning. The review leads to a taxonomy as well as the identification of the bast-studied cancer types, the best ensemble methods used for the related purposes, the prevailing input data types, the most common decision making strategies, and the common evaluating methodologies. Moreover, we establish future directions for researchers interested in following existing research trends or working on less-studied aspects of the area.

Integrating proteomics and explainable artificial intelligence: a comprehensive analysis of protein biomarkers for endometrial cancer diagnosis and prognosis

“…In many cases, the results of a single model may vary or tend to over fit. However, ensemble learning methods can reduce these risks and provide more consistent results ( Zhang et al, 2023 ; Zolfaghari et al, 2023 ). A stratified random sampling method was used to divide patients into a training set and a test set at a ratio of 80:20.…”

Enhancing Kitchen Activity Recognition: A Benchmark Study of the Rostock KTA Dataset

“…For our study we conducted a comprehensive systematic literature review, mirroring the approach outlined in [20], considering relevant AR studies within a kitchen environment. Our primary emphasis was on approaches developed or evaluated using openly accessible datasets, with a reliance on sensor data from ambient, wearable, cameras, objects and even radar and audio sources.…”

A robust approach for multi-type classification of brain tumor using deep feature fusion

“…Nevertheless, in recent years, with the emergence of deep learning theory, more and more researchers applied the deep learning theory into medical image processing ( Maurya et al, 2023 ). Deep learning has been employed widely in the analysis and diagnosis of diverse diseases ( Cao et al, 2021 ; Gu et al, 2021 ; Lin et al, 2022 ; Yang, 2022 ; Yao et al, 2022 ; Zolfaghari et al, 2023 ). Convolutional Neural Networks (CNNs) are widely recognized as one of the most prominent deep learning techniques.…”