A comparative study of breast cancer detection based on SVM and MLP BPN classifier

Ghosh, Soumadip; Mondal, Sujoy; Ghosh, Bhaskar

doi:10.1109/aces.2014.6808002

Cited by 40 publications

(10 citation statements)

References 10 publications

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“…It is determined by this measure that what percentage of all samples or what number of all samples are classified correctly. This is 1 of the most widely used classification evaluation measures which can be seen in Formula 6 10,33 Overall accuracy = TN TP TP FP TN FN…”

Section: Performance Evaluationmentioning

confidence: 99%

“…Given the importance of this issue, over the years, many studies have been conducted on issues such as calculating survival time, accuracy in diagnosis, and recurrence rate of breast cancer, 1,7-12 most of which have used public datasets (such as The surveillance, epidemiology and end results [SEER], Wisconsin), whose values have been simulated or have been belonged to a particular country’s population. Such data have problems such as the following:…”

Section: Introductionmentioning

confidence: 99%

“…4,5 According to a World Health Organization report, 80 of 1000 women are affected by this type of cancer every year, most of whom live in Third-World countries. 6 Given the importance of this issue, over the years, many studies have been conducted on issues such as calculating survival time, accuracy in diagnosis, and recurrence rate of breast cancer, 1,[7][8][9][10][11][12] most of which have used public datasets (such as The surveillance, epidemiology and end results [SEER], Wisconsin), whose values have been simulated or have been 1. Proposed models on benchmark datasets are not suitable for practical uses of other existing datasets and do not have the necessary efficiency.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Preprocessing Breast Cancer Data to Improve the Data Quality, Diagnosis Procedure, and Medical Care Services

2020

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In recent years, due to an increase in the incidence of different cancers, various data sources are available in this field. Consequently, many researchers have become interested in the discovery of useful knowledge from available data to assist faster decision-making by doctors and reduce the negative consequences of such diseases. Data mining includes a set of useful techniques in the discovery of knowledge from the data: detecting hidden patterns and finding unknown relations. However, these techniques face several challenges with real-world data. Particularly, dealing with inconsistencies, errors, noise, and missing values requires appropriate preprocessing and data preparation procedures. In this article, we investigate the impact of preprocessing to provide high-quality data for classification techniques. A wide range of preprocessing and data preparation methods are studied, and a set of preprocessing steps was leveraged to obtain appropriate classification results. The preprocessing is done on a real-world breast cancer dataset of the Reza Radiation Oncology Center in Mashhad with various features and a great percentage of null values, and the results are reported in this article. To evaluate the impact of the preprocessing steps on the results of classification algorithms, this case study was divided into the following 3 experiments: Breast cancer recurrence prediction without data preprocessing Breast cancer recurrence prediction by error removal Breast cancer recurrence prediction by error removal and filling null values Then, in each experiment, dimensionality reduction techniques are used to select a suitable subset of features for the problem at hand. Breast cancer recurrence prediction models are constructed using the 3 widely used classification algorithms, namely, naïve Bayes, k-nearest neighbor, and sequential minimal optimization. The evaluation of the experiments is done in terms of accuracy, sensitivity, F-measure, precision, and G-mean measures. Our results show that recurrence prediction is significantly improved after data preprocessing, especially in terms of sensitivity, F-measure, precision, and G-mean measures.

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Section: Performance Evaluationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Preprocessing Breast Cancer Data to Improve the Data Quality, Diagnosis Procedure, and Medical Care Services

2020

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“…In [12], an ANN (Artificial neural network) with Principal Component Analysis (PCA) is used.In [13], WPBC dataset is used for making comparison of different ML(Machine Learning) algorithms. The result showed that SVM, DT were among best predictors.In [14], multi-layer perceptron with backpropagationand support vector machine were used for classification of dataset. Support Vector machine was found to be the best result giving algorithm.…”

Section: Literature Surveymentioning

confidence: 99%

Machine Learning Techniques and Extreme Learning Machine for Early Breast Cancer Prediction

Gupta*¹,

Gill²

2020

IJITEE

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Breast Cancer is one of the most deadly disease and most of the women are infected by this vital disease in many parts of the world. Medical tests conducted in hospitals for determining the disease are very much expensive as well as timeconsuming. The problem can be resolved by diagnosing the problem in early spam of time and by providing results with more accuracy.In this paper, different machine learning and neural network algorithmhave been studied and compared to predict cancer in early stages so that life can be saved. The dataset available publically for Breast Cancer has been used. Different algorithms compared includeSupport Vector Machine Classification (SVM), K-Nearest NeighborClassification (KNN), Decision tree Classification (DT), Random Forest Classification (RF) and Extreme Learning Machine (ELM).All are compared on the basis of Accuracy and processing time are considered as the parameters for comparing analysis. The results reveal that extreme learning machine comes to be the better algorithm.

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“…In another work [11], the fusion of J48 and Multi Layered Perceptron Artificial Neural Network (MLP-ANN) with Principle Component Analysis (PCA) showed an accuracy of 97.57%. In addition, SVM was implemented with different approaches [12][13][14][15][16][17]. The best reported accuracy was 99.74% with 10-fold cross validation [15].…”

Section: Introductionmentioning

confidence: 99%

Smart Doctor: Performance of Supervised ART-I Artificial Neural Network for Breast Cancer Diagnoses

Al-Rawi

2020

eijs

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Wisconsin Breast Cancer Dataset (WBCD) was employed to show the performance of the Adaptive Resonance Theory (ART), specifically the supervised ART-I Artificial Neural Network (ANN), to build a breast cancer diagnosis smart system. It was fed with different learning parameters and sets. The best result was achieved when the model was trained with 50% of the data and tested with the remaining 50%. Classification accuracy was compared to other artificial intelligence algorithms, which included fuzzy classifier, MLP-ANN, and SVM. We achieved the highest accuracy with such low learning/testing ratio.

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A comparative study of breast cancer detection based on SVM and MLP BPN classifier

Cited by 40 publications

References 10 publications

Preprocessing Breast Cancer Data to Improve the Data Quality, Diagnosis Procedure, and Medical Care Services

Preprocessing Breast Cancer Data to Improve the Data Quality, Diagnosis Procedure, and Medical Care Services

Machine Learning Techniques and Extreme Learning Machine for Early Breast Cancer Prediction

Smart Doctor: Performance of Supervised ART-I Artificial Neural Network for Breast Cancer Diagnoses

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