Feature selection for medical diagnosis : Evaluation for cardiovascular diseases

Shilaskar, Swati; Ghatol, Ashok A.

doi:10.1016/j.eswa.2013.01.032

Cited by 186 publications

(61 citation statements)

References 12 publications

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“…The search terminates when no single attribute addition improves the current best score. The detailed algorithm is given in [27]. Table V lists performance of classifier based on individual and fusion feature sets.…”

Section: Support Vector Machine (Svm)mentioning

confidence: 99%

Feature enhancement for classifier optimization and dimensionality reduction

Shilaskar¹,

Ghatol²

2014

2014 Annual IEEE India Conference (INDICON)

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Voice is important for professionals like speakers, teachers, actors, singers and it is the important tool for communication. Laryngeal pathologies induce perturbations in the speech signal. Speech signal is discriminated as pathological or healthy based on roughness -breathiness -hoarseness (RBH) in the quality of signal. In recent years pattern recognition along with various signal processing techniques has emerged as an effective non invasive tool for diagnosis of pathological condition. Signal processing techniques tend to generate large number of features representing the signal. Automatic feature reduction techniques are vital in identifying the relevant features and eliminating the redundant ones. We extract features from speech signal using the acoustic analysis. Features are enhanced by alleviating gender bias. Periodic variations in the signal are captured using statistical techniques. We investigate intelligent system to generate reduced feature subset with improvement in diagnostic performance.

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Section: Support Vector Machine (Svm)mentioning

confidence: 99%

Feature enhancement for classifier optimization and dimensionality reduction

Shilaskar¹,

Ghatol²

2014

2014 Annual IEEE India Conference (INDICON)

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“…A few of the segmentation techniques were based on the Fuzzy Set Theory (Huang et al, 2007;Pietka et al, 2010), thresholding Gomathi and Thangaraj, 2010) and models (Mumcuoglu et al, 2011;Schilham et al, 2006). Detection approaches were based on the use of classifiers, such as artificial neural networks (Ashwin et al, 2012;Bevilacqua, 2013;García-Orellana et al, 2008;Itai et al, 2009;Kumar et al, 2011;Li et al, 2009;López et al, 2011;Mironică et al, 2011;Sasaki et al, 2010), k-nearest neighbor (AlAbsi et al, 2012;Li et al, 2009;Mironică et al, 2011;Sanchez et al, 2011;Schilham et al, 2006), support vectors machine (Grana et al, 2011;Li et al, 2009;Martinez-Murcia et al, 2014;Mironică et al, 2011;Miyaki et al, 2013;Segovia et al, 2012;Shilaskar and Ghatol, 2013;Wang et al, 2009) and probabilistic classifiers (Li et al, 2012;Liu et al, 2013;Mironică et al, 2011;Ramírez et al, 2009;Vertan et al, 2011).…”

Section: Tasks For Computer-aided Diagnosis Systemsmentioning

confidence: 99%

A systematic review on the evaluation and characteristics of computer-aided diagnosis systems

Gonçalves

Delamaro

Nunes

2014

Rev. Bras. Eng. Bioméd.

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Introduction: One of the challenges in developing Computer-Aided Diagnosis (CAD) systems is their accurate and comprehensive assessment. This paper presents the conduction and results of a systematic review (SR) that aims to verify the state of the art regarding the assessment of CAD systems. This survey provides a general analysis of the current status of the design, development and assessment of such systems and includes discussions on the most used metrics and approaches that could be utilized to obtain more objective evaluation methods. Methods: The SR was conducted using the scientifi c databases, ACM Digital Library, IEEE Xplore Digital Library, ScienceDirect and Web of Science. Inclusion and exclusion criteria were defi ned and applied to each retrieved work to select those of interest. From 156 studies retrieved, 100 studies were included. Results: There is a number of abnormalities that have been used for the development of CAD systems. Images from computed tomographies and mammographies are the most encountered types of medical images. Additionally, a number of studies used public databases for CAD evaluations. The main evaluation metrics and methods applied to CAD systems include sensitivity, accuracy, specifi city and receiver operating characteristic (ROC) analyses. In the assessed CAD systems that used the segmentation method, 30.0% applied the overlap measure. Discussion: There remain several topics to explore for the assessment of CAD schemes. While some evaluation metrics are traditionally used, they require a prior knowledge of case characteristics to test CAD systems. We were not able to identify articles that use software testing to evaluate CAD systems. Thus, we realize that there is a gap between CAD assessments and traditional practices of software engineering. However, the scope of this research is limited to scientifi c and academic works and excludes commercial interests. Finally, we discuss potential research studies within this scope to create a more objective and effi cient evaluation of CAD systems.

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“…Banking and Finance Sector [18], [19], [20], [21] Insurance risk Classification Insurance [22] Parkinson's disease , Medical Diagnosis of Cardio vascular disease, Cancer Diagnosis Prediction of antimicrobial peptides (Natural anti biotic) Breast cancer diagnosis Early detection of the Alzheimer's disease Brain tumor detection Medical Science, Bioinformatics [23], [24], [25], [26], [27], [32] Sentiment Analysis in multiple language Text Clustering Text Classification Emotion recognition from speech Spam Filtering…”

Section: Application / Business Domain Referencementioning

confidence: 99%

Feature Selection: A Practitioner View

Goswami¹,

Chakrabarti²

2014

IJITCS

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Abstract-Feature selection is one of the most important preprocessing steps in data mining and knowledge Engineering. In this short review paper, apart from a brief taxonomy of current feature selection methods, we review feature selection methods that are being used in practice. Subsequently we produce a near comprehensive list of problems that have been solved using feature selection across technical and commercial domain. This can serve as a valuable tool to practitioners across industry and academia. We also present empirical results of filter based methods on various datasets. The empirical study covers task of classification, regression, text classification and clustering respectively. We also compare filter based ranking methods using rank correlation.

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Feature selection for medical diagnosis : Evaluation for cardiovascular diseases

Cited by 186 publications

References 12 publications

Feature enhancement for classifier optimization and dimensionality reduction

Feature enhancement for classifier optimization and dimensionality reduction

A systematic review on the evaluation and characteristics of computer-aided diagnosis systems

Feature Selection: A Practitioner View

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