Detection of colon cancer based on microarray dataset using machine learning as a feature selection and classification techniques

Shafi, Aamir; Molla, M. M. Imran; Jui, Julakha Jahan; Rahman, Mohammad Motiur

doi:10.1007/s42452-020-3051-2

Cited by 34 publications

(13 citation statements)

References 20 publications

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“…Several other classifiers, such as AB, SVM, NN, and DT are less accurate because they overfit. Another application of this technique is as a means of selecting features based on their perceived importance (Shafi et al, 2020).…”

Section: Random Forestmentioning

confidence: 99%

A Comparative Study on Performance of Basic and Ensemble Classifiers With Various Datasets

Gunakala¹,

Shahid²

2023

acs

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Classification plays a critical role in machine learning (ML) systems for processing images, text and high -dimensional data. Predicting class labels from training data is the primary goal of classification. An optimal model for a particular classification problem is chosen on the basis of the model's performance and execution time. This paper compares and analyses the performance of basic as well as ensemble classifiers utilizing 10 -fold cross validation and also discusses their essential concepts, advantages, and disadvantages. In this study five basic classifiers namely Naïve Bayes (NB), Multi-layer Perceptron (MLP), Support Vector Machine (SVM), Decision Tree (DT), and Random Forest (RF) and the ensemble of all the five classifiers along with few more combinations are compared with five University of California Irvine (UCI) ML Repository datasets and a Diabetes Health Indicators dataset from kaggle repository. To analyze and compare the performance of classifiers, evaluation metrics like Accuracy, Recall, Precision, Area Under Curve (AUC) and F-Score are used. Experimental results showed that SVM performs best on two out of the six datasets (Diabetes Health Indicators and waveform), RF performs best for Arrhythmia, Sonar, Tic-tac-toe datasets, and the best ensemble combination is found to be DT+SVM+RF on Ionosphere dataset having respective accuracies 72.58%, 90.38%, 81.63%, 73.59%, 94.78% and 94.01% and the proposed ensemble combinations outperformed over the conventional models for few datasets.

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Section: Random Forestmentioning

confidence: 99%

A Comparative Study on Performance of Basic and Ensemble Classifiers With Various Datasets

Gunakala¹,

Shahid²

2023

acs

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“…However, this method failed to improve the performance by solving the computational complexity issues. Baliarsingh, S.K., et al [19] developed a gene selection approach using Enhanced Jaya Forest Optimization Algorithm (EJFOA) for classifying the cancer. At first, a statistical filter was utilized in order to sort the features, thereby generated the optimal feature subset.…”

Section: Related Workmentioning

confidence: 99%

“…Thus, the accuracy is the major concern diagnosis of CRC which should be improved by considering other parameters for the evaluation [10]. Machine learning approach was devised for improving the accuracy during cancer classification, but the major challenge lies in integrating this method with several other sophisticated techniques for the feature selection process in order to achieve efficient results [18].In [19], EJFOA was developed for the colon cancer classification. However, this method does not employ advanced machine learning approaches, such as reinforcement learning and deep learning in order to perform the gene selection and the classification process.…”

Section: Related Workmentioning

confidence: 99%

An Optimized Deep Learning Based Optimization Algorithm for the Detection of Colon Cancer Using Deep Recurrent Neural Networks

Ku¹,

Arulselvi²,

Sastry³

2023

Int. j. commun. netw. inf. secur.

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Colon cancer is the second leading dreadful disease-causing death. The challenge in the colon cancer detection is the accurate identification of the lesion at the early stage such that mortality and morbidity can be reduced. In this work, a colon cancer classification method is identified out using Dragonfly-based water wave optimization (DWWO) based deep recurrent neural network. Initially, the input cancer images subjected to carry a pre-processing, in which outer artifacts are removed. The pre-processed image is forwarded for segmentation then the images are converted into segments using Generative adversarial networks (GAN). The obtained segments are forwarded for attribute selection module, where the statistical features like mean, variance, kurtosis, entropy, and textual features, like LOOP features are effectively extracted. Finally, the colon cancer classification is solved by using the deep RNN, which is trained by the proposed Dragonfly-based water wave optimization algorithm. The proposed DWWO algorithm is developed by integrating the Dragonfly algorithm and water wave optimization.

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“…In (Hameed et al, 2018) proposed Least Absolute Shrinkage and Selection Operator is called LASSO for both high and datasets. A combination between Mean Decrease Accuracy (MDA) and Mean Decrease Gini (MDG) is called MDA-MDG (Shafi et al, 2020). In (Maldonado & López, 2018) proposed embedded method with two support vector machines (SVM) that extend the ideas of KP-SVM to Cost-Sensitive SVM (CS-SVM) and Support Vector Data Description (SVDD) for the class-imbalanced problem, which are called KP-CSSVM and KP-SVDD, respectively.…”

Section: Embedded-based Feature Selectionmentioning

confidence: 99%

A Survey of Feature Selection Strategies for DNA Microarray Classification

2023

CEIS

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Classification tasks are difficult and challenging in the bioinformatics field, that used to predict or diagnose patients at an early stage of disease by utilizing DNA microarray technology. However, crucial characteristics of DNA microarray technology are a large number of features and small sample sizes, which means the technology confronts a "dimensional curse" in its classification tasks because of the high computational execution needed and the discovery of biomarkers difficult. To reduce the dimensionality of features to find the significant features that can employ feature selection algorithms and not affect the performance of classification tasks. Feature selection helps decrease computational time by removing irrelevant and redundant features from the data. The study aims to briefly survey popular feature selection methods for classifying DNA microarray technology, such as filters, wrappers, embedded, and hybrid approaches. Furthermore, this study describes the steps of the feature selection process used to accomplish classification tasks and their relationships to other components such as datasets, crossvalidation, and classifier algorithms. In the case study, we chose four different methods of feature selection on two-DNA microarray datasets to evaluate and discuss their performances, namely classification accuracy, stability, and the subset size of selected features.

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Detection of colon cancer based on microarray dataset using machine learning as a feature selection and classification techniques

Cited by 34 publications

References 20 publications

A Comparative Study on Performance of Basic and Ensemble Classifiers With Various Datasets

A Comparative Study on Performance of Basic and Ensemble Classifiers With Various Datasets

An Optimized Deep Learning Based Optimization Algorithm for the Detection of Colon Cancer Using Deep Recurrent Neural Networks

A Survey of Feature Selection Strategies for DNA Microarray Classification

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