R-HEFS: Rough set based heterogeneous ensemble feature selection method for medical data classification

Bania, Rubul Kumar; Halder, Anindya

doi:10.1016/j.artmed.2021.102049

Cited by 41 publications

(12 citation statements)

References 44 publications

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“…Because the ICU observation data have many redundant features, the algorithm is more likely to suffer the curse of dimensionality 38 . Feature selection is a vital dimension reduction method for high-dimensional data 39 . Here, we employ a random forest model to select the vital features 35 .…”

Section: Methodsmentioning

confidence: 99%

A value-based deep reinforcement learning model with human expertise in optimal treatment of sepsis

et al. 2023

npj Digit. Med.

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Deep Reinforcement Learning (DRL) has been increasingly attempted in assisting clinicians for real-time treatment of sepsis. While a value function quantifies the performance of policies in such decision-making processes, most value-based DRL algorithms cannot evaluate the target value function precisely and are not as safe as clinical experts. In this study, we propose a Weighted Dueling Double Deep Q-Network with embedded human Expertise (WD3QNE). A target Q value function with adaptive dynamic weight is designed to improve the estimate accuracy and human expertise in decision-making is leveraged. In addition, the random forest algorithm is employed for feature selection to improve model interpretability. We test our algorithm against state-of-the-art value function methods in terms of expected return, survival rate, action distribution and external validation. The results demonstrate that WD3QNE obtains the highest survival rate of 97.81% in MIMIC-III dataset. Our proposed method is capable of providing reliable treatment decisions with embedded clinician expertise.

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Section: Methodsmentioning

confidence: 99%

A value-based deep reinforcement learning model with human expertise in optimal treatment of sepsis

et al. 2023

npj Digit. Med.

View full text Add to dashboard Cite

show abstract

“…Because the ICU observation data have many redundant features, the algorithm is more likely to suffer the curse of dimensionality 36 . Feature selection is a vital dimension reduction method for high-dimensional data 37 . Here, we employ a random forest model to select the vital features 33 .…”

Section: Methodsmentioning

confidence: 99%

WD3QNE: A value-based deep reinforcement learning model with human expertise in optimal treatment of sepsis

et al. 2022

Preprint

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Deep Reinforcement Learning (DRL) has been increasingly attempted in assisting clinicians for real-time treatment of sepsis. While a value function quantifies the performance of policies in such decision-making processes, most value-based DRL algorithms cannot evaluate the target value function precisely and are not as safe as clinical experts. In this study, we propose a Weighted Dueling Double Deep Q-Network with embedded human Expertise (WD3QNE) that incorporates a target Q value function with adaptive dynamic weight for enhanced policy improvement and human expertise in decision-making for sepsis treatment. In addition, the random forest algorithm is employed for feature selection to improve model interpretability. We test our algorithm against state-of-the-art value function methods in terms of expected return, survival rate and action distribution. The results demonstrate that WD3QNE obtains the highest survival rate of 97.81%. Our proposed method is capable of providing reliable treatment decisions with embedded clinician expertise.

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“…The IG [7], SRR, GR, OneR, mRMR, and CS feature selections are combined and used by the functional unit of the structure to produce the result. From the given datasets, the most relevant features are selected by this proposed EFFSM.…”

Section: Ensemble Feature Selectionmentioning

confidence: 99%

“…( A max-pooling layer is coupled to the output of the first conv layer which is described in Equation (7),…”

Section: Feature Extractionmentioning

confidence: 99%

Classification of Breast Cancer using Ensemble Filter Feature Selection with Triplet Attention Based Efficient Net Classifier

Bangalore Nagaraj,

Shivanandamurthy Hiremath,

Matta Polnaya

2024

IAJIT

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In medical imaging, the effective detection and classification of Breast Cancer (BC) is a current research important task because of the still existing difficulty to distinguish abnormalities from normal breast tissues due to their subtle appearance and ambiguous margins and distinguish abnormalities from the normal breast. Moreover, BC detection based on an automated detection model is needed, because manual diagnosis faces problems due to cost and shortage of skilled manpower, and also takes a very long time. Using deep learning and ensemble feature selection techniques, in this paper, a novel framework is introduced for classifying BC from histopathology images. The five primary steps of the suggested framework are as follows: 1) to make the largest original dataset and then deep learning model with data augmentation to improve the learning. 2) The best features are selected by an Ensemble Filter Feature selection Method (EFFM) which combines the best feature subsets to produce the final feature subsets. 3) Then the pruned Convolution Neural Network (CNN) model is utilized to extract the optimal features. 4) Finally, the classification is done through the Triplet Attention based Efficient Network (TAENet) classifier. The suggested model produces a 98% accuracy rate after being trained and tested on two different histopathology imaging datasets including images from four different data cohorts. Subsequently, the suggested strategy outperforms the conventional ones since the ensemble filter habitually acquires the best features, and experimental results demonstrate the importance of the proposed approach

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R-HEFS: Rough set based heterogeneous ensemble feature selection method for medical data classification

Cited by 41 publications

References 44 publications

A value-based deep reinforcement learning model with human expertise in optimal treatment of sepsis

A value-based deep reinforcement learning model with human expertise in optimal treatment of sepsis

WD3QNE: A value-based deep reinforcement learning model with human expertise in optimal treatment of sepsis

Classification of Breast Cancer using Ensemble Filter Feature Selection with Triplet Attention Based Efficient Net Classifier

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