A fuzzy-genetic approach to breast cancer diagnosis

Peña-Reyes, Carlos Andrés; Sipper, Moshe

doi:10.1016/s0933-3657(99)00019-6

Cited by 302 publications

(106 citation statements)

References 18 publications

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“…Other methods which have been utilized to determine the breast cancer diagnosis includes Fuzzy systems and Evolutionary algorithms. The fuzzy systems are used to represents different degrees of the disease (malignant or benign) a patient suffers from; on the other hand, the evolutionary algorithms are used to perform search to determine the most suitable fuzzy systems [3]. Isotonic separation which is a linear programming technique is based on the underlying assumption of maintaining same consistency in diagnosis.…”

Section: Literature Reviewmentioning

confidence: 99%

A Comparative Study of Different Machine Learning Algorithms for Disease Prediction

Romana

2017

IJARCSSE

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Abstract-Accurate diagnostic detection of the disease in a patient is critical and may alter the subsequent treatment and increase the chances of survival rate. Machine learning techniques have been instrumental in disease detection and are currently being used in various classification problems due to their accurate prediction performance. Various techniques may provide different desired accuracies and it is therefore imperative to use the most suitable method which provides the best desired results. This research seeks to provide comparative analysis of Support Vector Machine, Naïve bayes, J48 Decision Tree and neural network classifiers breast cancer and diabetes datsets.

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Section: Literature Reviewmentioning

confidence: 99%

A Comparative Study of Different Machine Learning Algorithms for Disease Prediction

Romana

2017

IJARCSSE

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“…Method Accuracy % Quinlan [27] C4.5 94.74 Hamiton et al [28] RAIC 95.00 Nauck and Kruse [29] NEFCLASS 95.06 Abonyi and Szeifert [9] SFC 95.57 Ster and Dobnikar [6] LDA 96.80 Goodman et al [30] AIRS 97.20 Pena-Reyes and Sipper [7] Fuzzy-GA1 97.36 Karabatak and Ince [13] AR + NN 97.40 Abbas [31] EANN 98.10 Setiono [8] Neuro-rule 98.10 Polat and Güneş [12] LS-SVM 98.53 Marcano et al [15] AMMLP 99.26 Hui et al [16] SVM + KK 99.41 Akay [2] SVM-CFS 99.51 Present study(80%-20% training-test) RS + ELM 100.00…”

Section: Authormentioning

confidence: 99%

A new intelligent classifier for breast cancer diagnosis based on a rough set and extreme learning machine: RS + ELM

Kaya¹

2013

Turk J Elec Eng & Comp Sci

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Breast cancer is one of the leading causes of death among women all around the world. Therefore, true and early diagnosis of breast cancer is an important problem. The rough set (RS) and extreme learning machine (ELM) methods were used collectively in this study for the diagnosis of breast cancer. The unnecessary attributes were discarded from the dataset by means of the RS approach. The classification process by means of ELM was performed using the remaining attributes. The Wisconsin Breast Cancer dataset (WBCD), derived from the University of California Irvine machine learning database, was used for the purpose of testing the proposed hybrid model and the success rate of the RS + ELM model was determined as 100%. Moreover, the most appropriate attributes for the diagnosis of breast cancer were determined from the WBCD in this study. It is considered that the proposed method will be useful in similar medical practices.

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“…Based on the literature review [1,[3][4][5][6][7][8][9][10][11][12], it is obvious that accuracy is the most popular metric for evaluating the performance of the classifier in breast cancer detection. Although the performance of classifier could be different on positive (malignant) and negative (benign) classes, the accuracy cannot make a distinction between false positives and false negatives, and so it does not show the performance of the classifier on positive and negative classes, separately [13].…”

Section: Introductionmentioning

confidence: 99%

The Performance of K-Nearest Neighbors on Malignant and Benign Classes: Sensitivity, Specificity, and Accuracy Analysis for Breast Cancer Diagnosis

Roshanpoor¹,

Ghazisaeidi²,

Kalhor³

et al. 2017

IJCA

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Breast cancer is one of the major threats to women nowadays. Early detection of breast cancer decreases mortality rate. Machine learning algorithms are used for this purpose. Accuracy is the most popular measure for evaluating machine learning algorithms for breast cancer diagnosis. However, it does not make a distinction between the performance of the classifier on malignant and benign test cases. This paper studies sensitivity and specificity along with accuracy to differentiate between KNN performance on malignant and benign classes for the different number of neighbors. Additionally, the standard deviations of sensitivity and specificity are studied to show KNN stability in malignant and benign classes. This study is critical because the cost of false negative is more than the cost of false positive in breast cancer detection. This study is conducted on Wisconsin breast cancer dataset (WBCD) from UCI repository. Stratified 10-fold cross-validation is used in this paper. Additionally, in order to increase the correctness of outcome, validation method repeated 100 times by considering that the samples are randomly reassigned to the folds again. The results show that KNN does not work well on malignant samples compared to the benign test cases, especially for higher values of neighbors. Also, the results for malignant samples are less reliable than benign ones. Furthermore, accuracy is more representative of specificity than sensitivity. It seems that the imbalance distributions of malignant and benign classes make difference between KNN performance on malignant and benign samples. It is recommended that a new study to be conducted to show the effect of imbalance numbers of positive and negative samples and also the difference between standard deviations of positive and negative classes on KNN performance.

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A fuzzy-genetic approach to breast cancer diagnosis

Cited by 302 publications

References 18 publications

A Comparative Study of Different Machine Learning Algorithms for Disease Prediction

A Comparative Study of Different Machine Learning Algorithms for Disease Prediction

A new intelligent classifier for breast cancer diagnosis based on a rough set and extreme learning machine: RS + ELM

The Performance of K-Nearest Neighbors on Malignant and Benign Classes: Sensitivity, Specificity, and Accuracy Analysis for Breast Cancer Diagnosis

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