The Wisconsin breast cancer problem: Diagnosis and TTR/DFS time prognosis using probabilistic and generalised regression information classifiers

Anagnostopoulos, Ioannis; Anagnostopoulos, Christos; Vergados, Dimitrios D.; Rouskas, Angelos; Kormentzas, George

doi:10.3892/or.15.4.975

Cited by 15 publications

(16 citation statements)

References 19 publications

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“…The sensitivity of 98.59% presented by MPD-FNA-Fuzzy is at the same level of prominence of other works using the same dataset with other techniques such as, for example, [11] and [14], using Probabilistic Neural Network-PNN with 31-568-2 topology. Although other works, for example, [11], [12][14], [15], are more accurate than MPD-FNA-Fuzzy, they use ten descriptors, while the MPD-FNA-Fuzzy uses only four descriptors, two of which are extracted indirectly from WDBC, which simplifies the model and streamlines processing.…”

Section: Resultssupporting

confidence: 55%

“…Although other works, for example, [11], [12][14], [15], are more accurate than MPD-FNA-Fuzzy, they use ten descriptors, while the MPD-FNA-Fuzzy uses only four descriptors, two of which are extracted indirectly from WDBC, which simplifies the model and streamlines processing.…”

Section: Resultsmentioning

confidence: 99%

“…Some examples of Computational Intelligence techniques are: Artificial Neural Networks, biological neuron-inspired technique [14,15]; Evolutionary Computation, inspired by biological evolution [12]; Expert Systems, inspired by inference process [11]; and Fuzzy Logic, inspired by language processing.…”

Section: Introductionmentioning

confidence: 99%

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Fuzzy method for pre-diagnosis of breast cancer from the Fine Needle Aspirate analysis

et al. 2012

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BackgroundAcross the globe, breast cancer is one of the leading causes of death among women and, currently, Fine Needle Aspirate (FNA) with visual interpretation is the easiest and fastest biopsy technique for the diagnosis of this deadly disease. Unfortunately, the ability of this method to diagnose cancer correctly when the disease is present varies greatly, from 65% to 98%. This article introduces a method to assist in the diagnosis and second opinion of breast cancer from the analysis of descriptors extracted from smears of breast mass obtained by FNA, with the use of computational intelligence resources - in this case, fuzzy logic.MethodsFor data acquisition of FNA, the Wisconsin Diagnostic Breast Cancer Data (WDBC), from the University of California at Irvine (UCI) Machine Learning Repository, available on the internet through the UCI domain was used. The knowledge acquisition process was carried out by the extraction and analysis of numerical data of the WDBC and by interviews and discussions with medical experts. The PDM-FNA-Fuzzy was developed in four steps: 1) Fuzzification Stage; 2) Rules Base; 3) Inference Stage; and 4) Defuzzification Stage. Performance cross-validation was used in the tests, with three databases with gold pattern clinical cases randomly extracted from the WDBC. The final validation was held by medical specialists in pathology, mastology and general practice, and with gold pattern clinical cases, i.e. with known and clinically confirmed diagnosis.ResultsThe Fuzzy Method developed provides breast cancer pre-diagnosis with 98.59% sensitivity (correct pre-diagnosis of malignancies); and 85.43% specificity (correct pre-diagnosis of benign cases). Due to the high sensitivity presented, these results are considered satisfactory, both by the opinion of medical specialists in the aforementioned areas and by comparison with other studies involving breast cancer diagnosis using FNA.ConclusionsThis paper presents an intelligent method to assist in the diagnosis and second opinion of breast cancer, using a fuzzy method capable of processing and sorting data extracted from smears of breast mass obtained by FNA, with satisfactory levels of sensitivity and specificity. The main contribution of the proposed method is the reduction of the variation hit of malignant cases when compared to visual interpretation currently applied in the diagnosis by FNA. While the MPD-FNA-Fuzzy features stable sensitivity at 98.59%, visual interpretation diagnosis provides a sensitivity variation from 65% to 98% (this track showing sensitivity levels below those considered satisfactory by medical specialists). Note that this method will be used in an Intelligent Virtual Environment to assist the decision-making (IVEMI), which amplifies its contribution.

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Section: Resultssupporting

confidence: 55%

Section: Resultsmentioning

confidence: 99%

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Fuzzy method for pre-diagnosis of breast cancer from the Fine Needle Aspirate analysis

et al. 2012

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“…There are 4 missing cases in lymph node status. The data have been previously used by Mangasarian et al (1995), Street et al (1995), Wolberg et al (1995a,b,c), and Anagnostopoulos et al (2006). This example focuses on analyzing the 32 real-valued input attributes.…”

Section: Real Data Examplementioning

confidence: 97%

Mixtures of commont-factor analyzers for modeling high-dimensional data with missing values

Wang

2015

Computational Statistics & Data Analysis

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“…Apart from neural networks several other methods have been also proposed, namely Support Vector Machines, Bayesian methods and Decision Trees, while recently, swarm intelligence and emergent algorithms are becoming more and more an alternative approach in the area. In [21] the authors compare their work with other 26 different approaches from the literature regarding the diagnosis problem. In the current work SVMs are introduced as an additional method for solving the prognosis breast cancer problem, employing a multi-class approach of 'one-against-all' as described in the following section.…”

Section: Related Workmentioning

confidence: 97%

An intelligent system for automated breast cancer diagnosis and prognosis using SVM based classifiers

2007

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In recent years, computational diagnostic tools and artificial intelligence techniques provide automated procedures for objective judgments by making use of quantitative measures and machine learning techniques. In this paper we propose a Support Vector Machines (SVMs) based classifier in comparison with Bayesian classifiers and Artificial Neural Networks for the prognosis and diagnosis of breast cancer disease. The paper provides the implementation details along with the corresponding results for all the assessed classifiers. Several comparative studies have been carried out concerning both the prognosis and diagnosis problem demonstrating the superiority of the proposed SVM algorithm in terms of sensitivity, specificity and accuracy.

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The Wisconsin breast cancer problem: Diagnosis and TTR/DFS time prognosis using probabilistic and generalised regression information classifiers

Cited by 15 publications

References 19 publications

Fuzzy method for pre-diagnosis of breast cancer from the Fine Needle Aspirate analysis

Fuzzy method for pre-diagnosis of breast cancer from the Fine Needle Aspirate analysis

Mixtures of commont-factor analyzers for modeling high-dimensional data with missing values

An intelligent system for automated breast cancer diagnosis and prognosis using SVM based classifiers

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