Automatic detection of heart disease using an artificial immune recognition system (AIRS) with fuzzy resource allocation mechanism and k-nn (nearest neighbour) based weighting preprocessing

Polat, Kemal; Şahan, Seral; Güneş, Salih

doi:10.1016/j.eswa.2006.01.027

Cited by 110 publications

(50 citation statements)

References 4 publications

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“…Polatet al proposed a new attribute weighting method based on fuzzy logic and called fuzzy weighting method in classification of ECG dataset [3]. Polat et al have proposed k-NN (k-nearest neighbor) based feature-weighting method to reduce the variance within features in dataset and applied to medical datasets [4][5][6]. Polat et al suggested a novel attribute weighting method based on similarity measure between features and applied to classification of Doppler signals to diagnose Atherosclerosis disease [7].…”

Section: Introductionmentioning

confidence: 98%

Application of Attribute Weighting Method Based on Clustering Centers to Discrimination of Linearly Non-Separable Medical Datasets

Polat

2011

J Med Syst

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In this paper, attribute weighting method based on the cluster centers with aim of increasing the discrimination between classes has been proposed and applied to nonlinear separable datasets including two medical datasets (mammographic mass dataset and bupa liver disorders dataset) and 2-D spiral dataset. The goals of this method are to gather the data points near to cluster center all together to transform from nonlinear separable datasets to linear separable dataset. As clustering algorithm, k-means clustering, fuzzy c-means clustering, and subtractive clustering have been used. The proposed attribute weighting methods are k-means clustering based attribute weighting (KMCBAW), fuzzy c-means clustering based attribute weighting (FCMCBAW), and subtractive clustering based attribute weighting (SCBAW) and used prior to classifier algorithms including C4.5 decision tree and adaptive neuro-fuzzy inference system (ANFIS). To evaluate the proposed method, the recall, precision value, true negative rate (TNR), G-mean1, G-mean2, f-measure, and classification accuracy have been used. The results have shown that the best attribute weighting method was the subtractive clustering based attribute weighting with respect to classification performance in the classification of three used datasets.

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Section: Introductionmentioning

confidence: 98%

Application of Attribute Weighting Method Based on Clustering Centers to Discrimination of Linearly Non-Separable Medical Datasets

Polat

2011

J Med Syst

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“…What's noteworthy of these milestones is the improvement in the accuracy when using hybrid techniques such as that in Polat et al [8], Ozsen and Gunes [9], Das et al [10], and Muhammed's [11] CLIP4 ensemble. The improvement was also pointed out in the Shouman et al [6], who also highlighted a necessity to conduct further research on hybrid techniques.…”

Section: Literature Surveymentioning

confidence: 99%

A Heart Disease Prediction Model using SVM-Decision Trees-Logistic Regression (SDL)

Mythili¹,

Mukherji²,

Padalia³

et al. 2013

IJCA

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The early prognosis of cardiovascular diseases can aid in making decisions to lifestyle changes in high risk patients and in turn reduce their complications. Research has attempted to pinpoint the most influential factors of heart disease as well as accurately predict the overall risk using homogenous data mining techniques. Recent research has delved into amalgamating these techniques using approaches such as hybrid data mining algorithms. This paper proposes a rule based model to compare the accuracies of applying rules to the individual results of support vector machine, decision trees, and logistic regression on the Cleveland Heart Disease Database in order to present an accurate model of predicting heart disease.

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“…This proposal is inspired by the human immune system behavior, comprising features such as recognition, memory acquisition, resource competition, diversity or self-tuning. Artificial immune systems have been successfully applied to heart, chest or thyroid disease diagnosis [23][24][25].…”

Section: State Of the Artmentioning

confidence: 99%

A Laboratory Test Expert System for Clinical Diagnosis Support in Primary Health Care

et al. 2015

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Clinical Decision Support Systems have the potential to reduce lack of communication and errors in diagnostic steps in primary health care. Literature reports have showed great advances in clinical decision support systems in the recent years, which have proven its usefulness in improving the quality of care. However, most of these systems are focused on specific areas of diseases. In this way, we propose a rule-based expert system, which supports clinicians in primary health care, providing a list of possible diseases regarding patient's laboratory tests results in order to assist previous diagnosis. Our system also allows storing and retrieving patient's data and the history of patient's analyses, establishing a basis for coordination between the various health care levels. A validation step and speed performance tests were made to check the quality of the system. We conclude that our system could improve clinician accuracy and speed, resulting in more efficiency and better quality of service. Finally, we propose some recommendations for further research.

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Automatic detection of heart disease using an artificial immune recognition system (AIRS) with fuzzy resource allocation mechanism and k-nn (nearest neighbour) based weighting preprocessing

Cited by 110 publications

References 4 publications

Application of Attribute Weighting Method Based on Clustering Centers to Discrimination of Linearly Non-Separable Medical Datasets

Application of Attribute Weighting Method Based on Clustering Centers to Discrimination of Linearly Non-Separable Medical Datasets

A Heart Disease Prediction Model using SVM-Decision Trees-Logistic Regression (SDL)

A Laboratory Test Expert System for Clinical Diagnosis Support in Primary Health Care

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