Feature selection and classification in breast cancer prediction using IoT and machine learning

Gopal, Vishak; Al‐Turjman, Fadi; Kumar, Rajagopal; Anand, L.; Rajesh, M.

doi:10.1016/j.measurement.2021.109442

Cited by 75 publications

(24 citation statements)

References 27 publications

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“…In this study, unlike previous studies, the step of detection and exclusion of outliers was added to the data set. When the table is examined, it is seen that the results of the studies with feature selection [13,[16][17][22][23][24] are considerably higher than the results of the studies without feature selection [14,[16][17][18][19][20][21][25][26]. PCA, KPCA, etc.…”

Section: Resultsmentioning

confidence: 99%

“…He reported that the SVM method is the most successful than other methods with a 77.63% accuracy rate on the Breast Cancer Data in Gaza Strip (BCDG) dataset. Gopal V.N., [16] used PCA for the feature selection method and three different classification methods LR, RF, and MLP. In the study of Gopal et al [16], the properties of the MLP classifier; It was determined as "learning rate: 0.001, max iteration: 200, tol: 0.0001, 10 cross-fold validation".…”

Section: Introductionmentioning

confidence: 99%

“…Gopal V.N., [16] used PCA for the feature selection method and three different classification methods LR, RF, and MLP. In the study of Gopal et al [16], the properties of the MLP classifier; It was determined as "learning rate: 0.001, max iteration: 200, tol: 0.0001, 10 cross-fold validation". These method accuracy rates were 79%, 95%, and 98% respectively.…”

Section: Introductionmentioning

confidence: 99%

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Breast Cancer Diagnosis with Machine Learning Techniques

DOĞAN

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Tanyıldızı

et al. 2022

Bitlis Eren Üniversitesi Fen Bilimleri Dergisi

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Cancer deaths are one of the highest rates of death. Although breast cancer is commonly associated with women, it is sometimes seen in men, and the mortality rate for men with breast cancer may be higher. The importance of early detection and treatment of breast cancer cannot be overstated. Cancer is diagnosed at an early stage thanks to expert systems, artificial intelligence, and machine learning approaches, and data analysis makes life easier for healthcare professionals. The nearest neighbor method, principal component analysis, neighborhood component method approaches were employed to detect breast cancer in this study. "Breast Cancer Wisconsin Diagnostic" database was used to create and test the approach. According to the results obtained, the highest success rate with 99.42% was obtained by using neighborhood component analysis and nearest neighbor classification algorithm method.

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Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Breast Cancer Diagnosis with Machine Learning Techniques

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Tanyıldızı

et al. 2022

Bitlis Eren Üniversitesi Fen Bilimleri Dergisi

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“…Gopal et al [15] suggested an approach for conducting earlier diagnoses of BC through the IoT and ML. The primary aim of the study was to examine ML techniques in forecasting BC using IoT devices.…”

Section: Prior Bc Diagnosis Models In a Smart Healthcare Environmentmentioning

confidence: 99%

Cognitive Computing-Based Mammographic Image Classification on an Internet of Medical

Mansour¹,

Althobaiti²

2022

Computers, Materials &Amp; Continua

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Recently, the Internet of Medical Things (IoMT) has become a research hotspot due to its various applicability in medical field. However, the data analysis and management in IoMT remain challenging owing to the existence of a massive number of devices linked to the server environment, generating a massive quantity of healthcare data. In such cases, cognitive computing can be employed that uses many intelligent technologies-machine learning (ML), deep learning (DL), artificial intelligence (AI), natural language processing (NLP) and others-to comprehend data expansively. Furthermore, breast cancer (BC) has been found to be a major cause of mortality among ladies globally. Earlier detection and classification of BC using digital mammograms can decrease the mortality rate. This paper presents a novel deep learning-enabled multi-objective mayfly optimization algorithm (DL-MOMFO) for BC diagnosis and classification in the IoMT environment. The goal of this paper is to integrate deep learning (DL) and cognitive computing-based techniques for e-healthcare applications as a part of IoMT technology to detect and classify BC. The proposed DL-MOMFO algorithm involved Adaptive Weighted Mean Filter (AWMF)-based noise removal and contrast-limited adaptive histogram equalisation (CLAHE)-based contrast improvement techniques to improve the quality of the digital mammograms. In addition, a U-Net architecture-based segmentation method was utilised to detect diseased regions in the mammograms. Moreover, a SqueezeNet-based feature extraction and a fuzzy support vector machine (FSVM) classifier were used in the presented technique. To enhance the diagnostic performance of the presented method, the MOMFO algorithm was used to effectively tune the parameters of the SqueezeNet and FSVM techniques. The DL-MOMFO technique was tested on the MIAS database, and the experimental outcomes revealed that the DL-MOMFO technique outperformed existing techniques.

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“…The main goal is to maximize the distance between this hyperplane and the data examples that are closest to it (support vectors) [ 45 ]. SVM is frequently applied in bioinformatics and medical analysis, especially for gene classification [ 46 ]. The DT model is used to create a training path to predict classes by deduction of the learning decision rules from the training dataset.…”

Section: Introductionmentioning

confidence: 99%

A Highly Discriminative Hybrid Feature Selection Algorithm for Cancer Diagnosis

Elemam

Elshrkawey

2022

The Scientific World Journal

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Cancer is a deadly disease that occurs due to rapid and uncontrolled cell growth. In this article, a machine learning (ML) algorithm is proposed to diagnose different cancer diseases from big data. The algorithm comprises a two-stage hybrid feature selection. In the first stage, an overall ranker is initiated to combine the results of three filter-based feature evaluation methods, namely, chi-squared, F-statistic, and mutual information (MI). The features are then ordered according to this combination. In the second stage, the modified wrapper-based sequential forward selection is utilized to discover the optimal feature subset, using ML models such as support vector machine (SVM), decision tree (DT), random forest (RF), and K-nearest neighbor (KNN) classifiers. To examine the proposed algorithm, many tests have been carried out on four cancerous microarray datasets, employing in the process 10-fold cross-validation and hyperparameter tuning. The performance of the algorithm is evaluated by calculating the diagnostic accuracy. The results indicate that for the leukemia dataset, both SVM and KNN models register the highest accuracy at 100% using only 5 features. For the ovarian cancer dataset, the SVM model achieves the highest accuracy at 100% using only 6 features. For the small round blue cell tumor (SRBCT) dataset, the SVM model also achieves the highest accuracy at 100% using only 8 features. For the lung cancer dataset, the SVM model also achieves the highest accuracy at 99.57% using 19 features. By comparing with other algorithms, the results obtained from the proposed algorithm are superior in terms of the number of selected features and diagnostic accuracy.

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Feature selection and classification in breast cancer prediction using IoT and machine learning

Cited by 75 publications

References 27 publications

Breast Cancer Diagnosis with Machine Learning Techniques

Breast Cancer Diagnosis with Machine Learning Techniques

Cognitive Computing-Based Mammographic Image Classification on an Internet of Medical

A Highly Discriminative Hybrid Feature Selection Algorithm for Cancer Diagnosis

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