Multi-stage modeling using fuzzy multi-criteria feature selection to improve survival prediction of ICU septic shock patients

Multiple-omics sequencing information with high-throughput has laid a solid foundation to identify genes associated with cancer prognostic process. Multiomics information study is capable of revealing the cancer occurring and developing system according to several aspects. Currently, the prognosis of osteosarcoma is still poor, so a genetic marker is needed for predicting the clinically related overall survival result. First, Office of Cancer Genomics (OCG Target) provided RNASeq, copy amount variations information, and clinically related follow-up data. Genes associated with prognostic process and genes exhibiting copy amount difference were screened in the training group, and the mentioned genes were integrated for feature selection with least absolute shrinkage and selection operator (Lasso). Eventually, effective biomarkers received the screening process. Lastly, this study built and demonstrated one gene-associated prognosis mode according to the set of the test and gene expression omnibus validation set; 512 prognosis-related genes ( P < 0.01), 336 copies of amplified genes ( P < 0.05), and 36 copies of deleted genes ( P < 0.05) were obtained, and those genes of the mentioned genomic variants display close associations with tumor occurring and developing mechanisms. This study generated 10 genes for candidates through the integration of genomic variant genes as well as prognosis-related genes. Six typical genes (i.e. MYC, CHIC2, CCDC152, LYL1, GPR142, and MMP27) were obtained by Lasso feature selection and stepwise multivariate regression study, many of which are reported to show a relationship to tumor progressing process. The authors conducted Cox regression study for building 6-gene sign, i.e. one single prognosis-related element, in terms of cases carrying osteosarcoma. In addition, the samples were able to be risk stratified in the training group, test set, and externally validating set. The AUC of five-year survival according to the training group and validation set reached over 0.85, with superior predictive performance as opposed to the existing researches. Here, 6-gene sign was built to be new prognosis-related marking elements for assessing osteosarcoma cases’ surviving state.

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A WCO Based Cancer Survival Prediction Using Statistical Feature Selection

International Journal of Advanced Trends in Computer Science and Engineering ◽

10.30534/ijatcse/2020/121942020 ◽

2020 ◽

Vol 9 (4) ◽

pp. 5029-5034

Author(s):

Sanku Rajendra Kumar

Keyword(s):

Feature Selection ◽

Cancer Survival ◽

Survival Prediction ◽

Statistical Feature

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Improving survival prediction using a novel feature selection and feature reduction framework based on the integration of clinical and molecular data

Biocomputing 2020 ◽

10.1142/9789811215636_0037 ◽

2019 ◽

Author(s):

Lisa Neums ◽

Richard Meier ◽

Devin C. Koestler ◽

Jeffrey A. Thompson

Keyword(s):

Feature Selection ◽

Molecular Data ◽

Feature Reduction ◽

Survival Prediction

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Circulating Endothelial Cells From Septic Shock Patients Convert to Fibroblasts Are Associated With the Resuscitation Fluid Dose and Are Biomarkers for Survival Prediction

Critical Care Medicine ◽

10.1097/ccm.0000000000003778 ◽

2019 ◽

Vol 47 (7) ◽

pp. 942-950 ◽

Cited By ~ 5

Author(s):

Pablo Tapia ◽

Sebastian Gatica ◽

Cristian Cortés-Rivera ◽

Carolina Otero ◽

Alvaro Becerra ◽

...

Keyword(s):

Septic Shock ◽

Endothelial Cells ◽

Circulating Endothelial Cells ◽

Survival Prediction ◽

Resuscitation Fluid

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Multi-Stage Feature Selection (MSFS) Algorithm for UWB-Based Early Breast Cancer Size Prediction

10.1101/2020.02.06.936831 ◽

2020 ◽

Author(s):

V. Vijayasarveswari ◽

A.M. Andrew ◽

M. Jusoh ◽

T. Sabapathy ◽

R.A.A. Raof ◽

...

Keyword(s):

Breast Cancer ◽

Neural Network ◽

Support Vector Machine ◽

Feature Selection ◽

Probabilistic Neural Network ◽

The Other ◽

Ultra Wideband ◽

Support Vector ◽

Data Normalization ◽

Multi Stage

AbstractBreast cancer is the most common cancer among women and it is one of the main causes of death for women worldwide. To attain an optimum medical treatment for breast cancer, an early breast cancer detection is crucial. This paper proposes a multistage feature selection method that extracts statistically significant features for breast cancer size detection using proposed data normalization techniques. Ultra-wideband (UWB) signals, controlled using microcontroller are transmitted via an antenna from one end of the breast phantom and are received on the other end. These ultra-wideband analogue signals are represented in both time and frequency domain. The preprocessed digital data is passed to the proposed multi-stage feature selection algorithm. This algorithm has four selection stages. It comprises of data normalization methods, feature extraction, data dimensional reduction and feature fusion. The output data is fused together to form the proposed datasets, namely, 8-HybridFeature, 9-HybridFeature and 10-HybridFeature datasets. The classification performance of these datasets is tested using the Support Vector Machine, Probabilistic Neural Network and Naïve Bayes classifiers for breast cancer size classification. The research findings indicate that the 8-HybridFeature dataset performs better in comparison to the other two datasets. For the 8-HybridFeature dataset, the Naïve Bayes classifier (91.98%) outperformed the Support Vector Machine (90.44%) and Probabilistic Neural Network (80.05%) classifiers in terms of classification accuracy. The finalized method is tested and visualized in the MATLAB based 2D and 3D environment.

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Multi-Stage Recognition of Speech Emotion Using Sequential Forward Feature Selection

Electrical Control and Communication Engineering ◽

10.1515/ecce-2016-0005 ◽

2016 ◽

Vol 10 (1) ◽

pp. 35-41 ◽

Cited By ~ 1

Author(s):

Tatjana Liogienė ◽

Gintautas Tamulevičius

Keyword(s):

Feature Selection ◽

Emotion Recognition ◽

Classification Scheme ◽

Recognition Rate ◽

Single Stage ◽

Speech Emotion Recognition ◽

Forward Selection ◽

Multi Stage ◽

Stage Scheme ◽

Stage Classification

Abstract The intensive research of speech emotion recognition introduced a huge collection of speech emotion features. Large feature sets complicate the speech emotion recognition task. Among various feature selection and transformation techniques for one-stage classification, multiple classifier systems were proposed. The main idea of multiple classifiers is to arrange the emotion classification process in stages. Besides parallel and serial cases, the hierarchical arrangement of multi-stage classification is most widely used for speech emotion recognition. In this paper, we present a sequential-forward-feature-selection-based multi-stage classification scheme. The Sequential Forward Selection (SFS) and Sequential Floating Forward Selection (SFFS) techniques were employed for every stage of the multi-stage classification scheme. Experimental testing of the proposed scheme was performed using the German and Lithuanian emotional speech datasets. Sequential-feature-selection-based multi-stage classification outperformed the single-stage scheme by 12–42 % for different emotion sets. The multi-stage scheme has shown higher robustness to the growth of emotion set. The decrease in recognition rate with the increase in emotion set for multi-stage scheme was lower by 10–20 % in comparison with the single-stage case. Differences in SFS and SFFS employment for feature selection were negligible.

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