A comprehensive data‐level investigation of cancer diagnosis on imbalanced data

Gupta, Surbhi; Gupta, Manoj Kumar

doi:10.1111/coin.12452

Cited by 33 publications

(8 citation statements)

References 52 publications

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“…The ensemble architecture suggested in the study performed significantly and achieved a 99% AUC score. An empirical study was carried out in 2021 [ 47 ] which offered a novel classification approach based on ensemble learning. The ensemble algorithm was evaluated on five benchmark datasets from the UCI repository.…”

Section: Literature Surveymentioning

confidence: 99%

Prediction Performance of Deep Learning for Colon Cancer Survival Prediction on SEER Data

Gupta

Kalaivani

Rajasundaram

et al. 2022

BioMed Research International

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Colon and rectal cancers are the most common kinds of cancer globally. Colon cancer is more prevalent in men than in women. Early detection increases the likelihood of survival, and treatment significantly increases the likelihood of eradicating the disease. The Surveillance, Epidemiology, and End Results (SEER) programme is an excellent source of domestic cancer statistics. SEER includes nearly 30% of the United States population, covering various races and geographic locations. The data are made public via the SEER website when a SEER limited-use data agreement form is submitted and approved. We investigate data from the SEER programme, specifically colon cancer statistics, in this study. Our objective is to create reliable colon cancer survival and conditional survival prediction algorithms. In this study, we have presented an overview of cancer diagnosis methods and the treatments used to cure cancer. This paper presents an analysis of prediction performance of multiple deep learning approaches. The performance of multiple deep learning models is thoroughly examined to discover which algorithm surpasses the others, followed by an investigation of the network’s prediction accuracy. The simulation outcomes indicate that automated prediction models can predict colon cancer patient survival. Deep autoencoders displayed the best performance outcomes attaining 97% accuracy and 95% area under curve-receiver operating characteristic (AUC-ROC).

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Section: Literature Surveymentioning

confidence: 99%

Prediction Performance of Deep Learning for Colon Cancer Survival Prediction on SEER Data

Gupta

Kalaivani

Rajasundaram

et al. 2022

BioMed Research International

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“…XGBoost, a scalable tree boosting approach, has been used for cervical cancer risk prediction (Gupta, and Gupta, 2022). The tuned model employs regularization and integrates sparse-aware, and quantile methods to handle missing data (Jha et al, 2021).…”

Section: Extreme Gradient Boostingmentioning

confidence: 99%

SKCV: Stratified K-fold cross-validation on ML classifiers for predicting cervical cancer

Prusty

Patnaik

Dash

2022

Front. Nanotechnol.

102

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Cancer is the unregulated development of abnormal cells in the human body system. Cervical cancer, also known as cervix cancer, develops on the cervix’s surface. This causes an overabundance of cells to build up, eventually forming a lump or tumour. As a result, early detection is essential to determine what effective treatment we can take to overcome it. Therefore, the novel Machine Learning (ML) techniques come to a place that predicts cervical cancer before it becomes too serious. Furthermore, four common diagnosis testing namely, Hinselmann, Schiller, Cytology, and Biopsy have been compared and predicted with four common ML models, namely Support Vector Machine (SVM), Random Forest (RF), K-Nearest Neighbors (K-NNs), and Extreme Gradient Boosting (XGB). Additionally, to enhance the better performance of ML models, the Stratified k-fold cross-validation (SKCV) method has been implemented over here. The findings of the experiments demonstrate that utilizing an RF classifier for analyzing the cervical cancer risk, could be a good alternative for assisting clinical specialists in classifying this disease in advance.

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“…Convolution and pooling are the following steps, which are then put into a fully connected multi-layer perceptron. The last layer, known as the output layer, recognizes the image's characteristics using back-propagation techniques (Gupta and Gupta, 2021a). Because of its unique properties, such as local connection and shared weights, CNN increases the system's accuracy and performance.…”

Section: Figurementioning

confidence: 99%

“…The advent of deep learning has profoundly affected a wide variety of machine learning applications and research. Few of such studies (Gupta and Gupta, 2021a), (Gupta and Gupta, 2021b), (Gupta and Gupta, 2021c) are described in this section. The work flow used for classification of cancer data is shown in Figure 11.…”

Section: Optimization With Decay Ratesmentioning

confidence: 99%

Deep learning techniques for cancer classification using microarray gene expression data

Gupta

Gupta²,

Shabaz³

et al. 2022

Front. Physiol.

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Cancer is one of the top causes of death globally. Recently, microarray gene expression data has been used to aid in cancer’s effective and early detection. The use of DNA microarray technology to uncover information from the expression levels of thousands of genes has enormous promise. The DNA microarray technique can determine the levels of thousands of genes simultaneously in a single experiment. The analysis of gene expression is critical in many disciplines of biological study to obtain the necessary information. This study analyses all the research studies focused on optimizing gene selection for cancer detection using artificial intelligence. One of the most challenging issues is figuring out how to extract meaningful information from massive databases. Deep Learning architectures have performed efficiently in numerous sectors and are used to diagnose many other chronic diseases and to assist physicians in making medical decisions. In this study, we have evaluated the results of different optimizers on a RNA sequence dataset. The Deep learning algorithm proposed in the study classifies five different forms of cancer, including kidney renal clear cell carcinoma (KIRC), Breast Invasive Carcinoma (BRCA), lung adenocarcinoma (LUAD), Prostate Adenocarcinoma (PRAD) and Colon Adenocarcinoma (COAD). The performance of different optimizers like Stochastic gradient descent (SGD), Root Mean Squared Propagation (RMSProp), Adaptive Gradient Optimizer (AdaGrad), and Adaptive Momentum (AdaM). The experimental results gathered on the dataset affirm that AdaGrad and Adam. Also, the performance analysis has been done using different learning rates and decay rates. This study discusses current advancements in deep learning-based gene expression data analysis using optimized feature selection methods.

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A comprehensive data‐level investigation of cancer diagnosis on imbalanced data

Cited by 33 publications

References 52 publications

Prediction Performance of Deep Learning for Colon Cancer Survival Prediction on SEER Data

Prediction Performance of Deep Learning for Colon Cancer Survival Prediction on SEER Data

SKCV: Stratified K-fold cross-validation on ML classifiers for predicting cervical cancer

Deep learning techniques for cancer classification using microarray gene expression data

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