Integrating Feature Selection and Feature Extraction Methods With Deep Learning to Predict Clinical Outcome of Breast Cancer

Zhang, Dejun; Zou, Lu; Zhou, Xionghui; He, Fazhi

doi:10.1109/access.2018.2837654

Cited by 120 publications

(51 citation statements)

References 26 publications

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“…The experimental results of GKNE-LGBC technique and existing methods PCA-AE-Ada [1] and deep learning-based multi-model ensemble method [2] are compared in this section with different parameters such as breast cancer detection accuracy, false positive rate, and time complexity. The obtained results are discussed with the help of either table or graphical representation.…”

Section: Comparative Analyses Under Different Parameters and Resultsmentioning

confidence: 99%

“…Experimental evaluations of proposed GKNE-LGBC and existing methods PCA-AE-Ada [1] and deep learning-based multi-model ensemble method [2] are performed using Java language with breast cancer microarray dataset taken from the http://csse.szu.edu.cn/staff/zhuzx/Datasets.html. This dataset comprises the 244881 attributes and 97 instances.…”

Section: Experimental Evaluation and Parameter Settingsmentioning

confidence: 99%

“…For the experimental evaluation, the numbers of gene expression data are taken as input for identifying breast cancer. The performances of GKNE-LGBC technique and existing methods PCA-AE-Ada [1] and deep learning-based multimodel ensemble method [2] are evaluated with the different parameters such as breast cancer detection accuracy, false positive rate, and time complexity. The experimental result of proposed GKNE-LGBC technique is compared with two existing techniques.…”

Section: Experimental Evaluation and Parameter Settingsmentioning

confidence: 99%

“…A principal component analysis and autoencoder neural network with the AdaBoost algorithm (PCA-AE-Ada) was designed in [1] for efficiently detects the clinical outcomes of cancer patients with gene expression profile. The designed algorithm failed to identify which features are most relevant for cancer prediction.…”

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confidence: 99%

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Neighbor Embedding Feature Selected Light Gradient Boosting Classification for Breast Cancer Detection with Gene Expression Data

2019

IJITEE

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Breast cancer is one of the most frequently diagnosed cancers among women worldwide. Accurate detection of Breast cancer is essential for providing better treatment and risk minimization of the patients. Recently, the collection of biological data like gene expression, protein sequences, DNA sequences are used due to improvements of accessible data mining techniques to diagnosis the disease at an earlier stage. The current state-of-art methods reported to have certain limitations in their diagnostic capability. In order to improve the breast cancer classification, an efficient technique called Gaussian Kernelized Neighbor Embedding based Light Gradient Boost Classification (GKNE-LGBC) technique is introduced. The GKNE-LGBC technique considers the benchmark microarray dataset and performs two processes such as feature selection and classification for detecting breast cancer using gene expression data. The number of gene and the data are collected from the microarray dataset. After collecting, the Gaussian Kernelized stochastic neighbor embedding algorithm is applied to select the relevant features (i.e. genes) and remove the irrelevant features based on the distance similarity. Next, the classification of the gene expression data is done with the help of steepest descent light gradient boosting algorithm. The boosting algorithm initially constructs’ number of weak learners i.e. bivariate regression tree to classify the input expression data into normal or cancerous with the selected features. Then the weak classifiers are combined into strong by minimizing the training error. This helps to improve breast cancer detection accuracy and minimizes the false positive rate. The experimental evaluation is carried out using gene microarray dataset with various parameters such as breast cancer detection accuracy, false positive rate and breast cancer detection time with a number of genes. The experimental results confirm that the proposed GKNE-LGBC technique accurately identifies breast cancer with higher accuracy, and minimal time complexity as well as false positive rate as compared to the state-of-art- methods.

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Section: Comparative Analyses Under Different Parameters and Resultsmentioning

confidence: 99%

Section: Experimental Evaluation and Parameter Settingsmentioning

confidence: 99%

Section: Experimental Evaluation and Parameter Settingsmentioning

confidence: 99%

mentioning

confidence: 99%

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Neighbor Embedding Feature Selected Light Gradient Boosting Classification for Breast Cancer Detection with Gene Expression Data

2019

IJITEE

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“…Although a majority of the methods applying deep learning in cancer research are for processing images (e.g. mammograms) [16] [17] [18] [19] and gene expression profiles [20][21] [22], more recently, deep-learning based NLP systems are gaining prominence for cancer information extraction from EHRs [23][24] [25] [26] [27]. For example, Gao et al [26] implemented a hierarchical attention network for extracting some of the crucial clinical oncology data elements such as primary cancer site and histological grade which are gathered by cancer registries.…”

Section: Introductionmentioning

confidence: 99%

A frame semantic overview of NLP-based information extraction for cancer-related EHR notes

Datta

Bernstam

Roberts

2019

Journal of Biomedical Informatics

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Objective:There is a lot of information about cancer in Electronic Health Record (EHR) notes that can be useful for biomedical research provided natural language processing (NLP) methods are available to extract and structure this information. In this paper, we present a scoping review of existing clinical NLP literature for cancer. Methods:We identified studies describing an NLP method to extract specific cancer-related information from EHR sources from PubMed, Google Scholar, ACL Anthology, and existing reviews. Two exclusion criteria were used in this study. We excluded articles where the extraction techniques used were too broad to be represented as frames (e.g., document classification) and also where very low-level extraction methods were used (e.g. simply identifying clinical concepts). 79 articles were included in the final review. We organized this information according to frame semantic principles to help identify common areas of overlap and potential gaps.Results: Frames were created from the reviewed articles pertaining to cancer information such as cancer diagnosis, tumor description, cancer procedure, breast cancer diagnosis, prostate cancer diagnosis and pain in prostate cancer patients. These frames included both a definition as well as specific frame elements (i.e. extractable attributes). We found that cancer diagnosis was the most common frame among the reviewed papers (36 out of 79), with recent work focusing on extracting information related to treatment and breast cancer diagnosis. Conclusion:The list of common frames described in this paper identifies important cancerrelated information extracted by existing NLP techniques and serves as a useful resource for future researchers requiring cancer information extracted from EHR notes. We also argue, due to the heavy duplication of cancer NLP systems, that a general purpose resource of annotated cancer frames and corresponding NLP tools would be valuable.

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Supervising Healthcare Schemes Using Machine Learning in Breast Cancer and Internet of Things (SHSMLIoT)

Lamba

Munjal

Gigras

2022

Internet of Healthcare Things

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Integrating Feature Selection and Feature Extraction Methods With Deep Learning to Predict Clinical Outcome of Breast Cancer

Cited by 120 publications

References 26 publications

Neighbor Embedding Feature Selected Light Gradient Boosting Classification for Breast Cancer Detection with Gene Expression Data

Neighbor Embedding Feature Selected Light Gradient Boosting Classification for Breast Cancer Detection with Gene Expression Data

A frame semantic overview of NLP-based information extraction for cancer-related EHR notes

Supervising Healthcare Schemes Using Machine Learning in Breast Cancer and Internet of Things (SHSMLIoT)

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