Breast cancer classification using machine learning

Amrane, Meriem; Oukid, Saliha; Gagaoua, Ikram; Ensari, Tolga

doi:10.1109/ebbt.2018.8391453

Cited by 275 publications

(101 citation statements)

References 18 publications

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“…e results of employing 12 different types of SVM were 97.89% for variation and 33.34% for accuracy. Amrane et al [19] proposed two different classifiers (knearest neighbors (KNN) and Naïve Bayes (NB)) to diagnose breast cancer. e results showed that KNN achieved the highest accuracy of 97.51%, and the lowest accuracy of NB was 96.19%.…”

Section: Introductionmentioning

confidence: 99%

Tabu Search and Machine-Learning Classification of Benign and Malignant Proliferative Breast Lesions

Dhahri

Rahmany

Mahmood

et al. 2020

BioMed Research International

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Breast cancer is the most diagnosed cancer among women around the world. The development of computer-aided diagnosis tools is essential to help pathologists to accurately interpret and discriminate between malignant and benign tumors. This paper proposes the development of an automated proliferative breast lesion diagnosis based on machine-learning algorithms. We used Tabu search to select the most significant features. The evaluation of the feature is based on the dependency degree of each attribute in the rough set. The categorization of reduced features was built using five machine-learning algorithms. The proposed models were applied to the BIDMC-MGH and Wisconsin Diagnostic Breast Cancer datasets. The performance measures of the used models were evaluated owing to five criteria. The top performing models were AdaBoost and logistic regression. Comparisons with others works prove the efficiency of the proposed method for superior diagnosis of breast cancer against the reviewed classification techniques.

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Section: Introductionmentioning

confidence: 99%

Tabu Search and Machine-Learning Classification of Benign and Malignant Proliferative Breast Lesions

Dhahri

Rahmany

Mahmood

et al. 2020

BioMed Research International

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“…Results showed that when compared to NaiveBayes classifier with an accuracy rate of 96.19%, the K-nearest neighbour gives a higher accuracy of 97.51% with lower error rate. [29] Yomna Omar, et al, have created a Lung Cancer Prognosis System (LCPS). Oncologists can use this system to evaluate accurately their patients' health condition.…”

Section: Literature Reviewmentioning

confidence: 99%

Improving Classifier Accuracy for diagnosing Chronic Kidney Disease Using Support Vector Machines

Kumar*¹,

Thangaraju²

2019

IJEAT

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Preventing Chronic Kidney Disease has become one of the most intriguing task to the healthcare society. The major objective of this paper is to deal mainly with different classification algorithms namely NaiveBayes, Multi Layer Perceptron and Support Vector Machine. The work analyzes the Chronic Kidney Disease dataset taken from the machine learning repository of UCI. Pre-processing techniques such as missing value replacement, unsupervised discretization and normalization are applied to the Chronic Kidney Disease dataset to improve accuracy. Accuracy and time are the taken as the experimental outcomes of the classification models. The final conclusion states that Support Vector Machine implements much superior than all the other classification methods.

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“…Meriem Amrane [4], have evaluated diverse classifier algorithms on the Wisconsin Breast Cancer diagnosis dataset. The Breast Cancer Dataset (BCD) that they used is donated to the University of California, Irvine (UCI).…”

Section: Literature Survrymentioning

confidence: 99%

Breast Cancer Classification using Nature-inspired Algorithm

2019

IJRTE

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Breast Cancer is one among the dangerous ailments that roots up of deaths among women worldwide. Lots of risk factors have been identified through research though the exact reasons of breast cancer are not yet fully understood The Artificial Immune Recognition System classifier helps to classify the type of breast cancer on Wisconsin dataset which provides accurate prediction of the classes of breast cancer. i.e, Benign and Malignant. This system focuses on supervised classification with the help of clonal selection algorithm, Hierarchical Learning Vector Quantization, Multipass Self Organizing Map. The goal of this system can implement the algorithm to classify the cancer accurately and to compare the error rate, f-measure with previous classification algorithms.

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Breast cancer classification using machine learning

Cited by 275 publications

References 18 publications

Tabu Search and Machine-Learning Classification of Benign and Malignant Proliferative Breast Lesions

Tabu Search and Machine-Learning Classification of Benign and Malignant Proliferative Breast Lesions

Improving Classifier Accuracy for diagnosing Chronic Kidney Disease Using Support Vector Machines

Breast Cancer Classification using Nature-inspired Algorithm

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