An Empirical Comparison Of Supervised Learning Algorithms In Disease Detection

Aruna, S.; Rajagopalan,; Nandakishore,

doi:10.5121/ijitcs.2011.1408

Cited by 19 publications

(14 citation statements)

References 30 publications

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“…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%

“…A growing area of research relates to the use of techniques from Computational Intelligence (CI) applied to the processing of information necessary for the medical diagnosis. We can cite as examples, [3-15]. …”

Section: Introductionmentioning

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: Resultsmentioning

confidence: 99%

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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“…There are a number of machine learning and statistical modeling approaches that so far have been involved in various aspects of solving the problem. According to [3], although other classifiers perform well, the SVM outperforms other classifiers with respect to accuracy, sensitivity, specificity, and precision. This paper presents a joint Matlab implementation of the SVM and Naïve Bayes methods in a new dataset acquired from the patients examined for diabetes in Kosovo.…”

Section: Introductionmentioning

confidence: 98%

An intelligent system for diabetes prediction

Tafa

Pervetica

Karahoda

2015

2015 4th Mediterranean Conference on Embedded Computing (MECO)

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With the emerging increase of diabetes, that recently affects around 346 million people, of which more than one-third go undetected in early stage, a strong need for supporting the medical decision-making process is generated. A number of researches have focused either in using one of the algorithms or in the comparisons of the performances of algorithms on a given, usually predefined and static datasets that are accessible through the Internet. This paper focuses on the joint implementation of the support vector machine (SVM) and Naïve Bayes statistical modeling, in the dataset acquired from the medical examinations of 402 patients, in order to improve the computer-supported diagnosis reliability. The dataset contains some attributes that have not been previously used in computer-based evaluations. The results show that the joint implementation of two algorithms improves significantly the overall reliability of the system outcome, which is crucial in the computer-supported diabetes diagnostic process.

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“…More xploiting the same en after[29]) cross Weka. They are terms of confusion10 in terms of four the proportion of algorithm Fig.…”

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confidence: 99%

A decision theory based tool for detection of encrypted WebRTC traffic

Mauro

Longo

2015

2015 18th International Conference on Intelligence in Next Generation Networks

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The detection of encrypted streamed traffic (like VoIP or Video) is an increasingly important issue for authorities involved in lawful interception. Aside from well established technologies like Skype, Facetime and MSN Messenger a new one is recently spreading: Web Real-Time Communication (WebRTC), which, with the support of powerful encryption methods as DTLS, offers capabilities for encrypted streaming voice and video without the need of installing a specific application but using a common browser like Chrome, Firefox or Opera. WebRTC traffic cannot be detected through methods of semantic recognition since it does not exhibit a distinguishable sequence of information pieces and hence statistical recognition methods are called for. In this paper we propose and evaluate a decision theory based system allowing to recognize encrypted WebRTC traffic by means of an open-source machine learning environment: Weka.

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An Empirical Comparison Of Supervised Learning Algorithms In Disease Detection

Abstract: ABSTRACT

Cited by 19 publications

References 30 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

An intelligent system for diabetes prediction

A decision theory based tool for detection of encrypted WebRTC traffic

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