An ensemble PSO-based approach for diagnosis of coronary artery disease

Hedeshi, Najmeh Ghadiri; Abadeh, Mohammad Saniee

doi:10.1109/aisp.2011.5960975

Cited by 7 publications

(4 citation statements)

References 17 publications

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“…Moreover, the average length of rules is 3.01. These results show that the interpretability of the proposed method is very high (in comparison to [ 18 , 33 , 34 ], whose methods produce more number of if-then rules to detect CAD).…”

Section: Discussionmentioning

confidence: 90%

“…In our previous works [ 18 , 32 ], in each run, one rule was extracted and added to rule set. In this work, each individual in PSO algorithm includes a set of rules.…”

Section: Discussionmentioning

confidence: 99%

“…In previous work [ 18 ], we used one uniform fuzzy partition for all attributes, while we know that these attributes may be of different kinds (e.g., continues, ordinal, and ratio). Therefore, it is more appropriate to apply several fuzzy partitions for various types of attributes.…”

Section: Introductionmentioning

confidence: 99%

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Coronary Artery Disease Detection Using a Fuzzy-Boosting PSO Approach

Hedeshi

Abadeh

2014

Computational Intelligence and Neuroscience

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In the past decades, medical data mining has become a popular data mining subject. Researchers have proposed several tools and various methodologies for developing effective medical expert systems. Diagnosing heart diseases is one of the important topics and many researchers have tried to develop intelligent medical expert systems to help the physicians. In this paper, we propose the use of PSO algorithm with a boosting approach to extract rules for recognizing the presence or absence of coronary artery disease in a patient. The weight of training examples that are classified properly by the new rules is reduced by a boosting mechanism. Therefore, in the next rule generation cycle, the focus is on those fuzzy rules that account for the currently misclassified or uncovered instances. We have used coronary artery disease data sets taken from University of California Irvine, (UCI), to evaluate our new classification approach. Results show that the proposed method can detect the coronary artery disease with an acceptable accuracy. Also, the discovered rules have significant interpretability as well.

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Section: Discussionmentioning

confidence: 90%

“…In our previous works [ 18 , 32 ], in each run, one rule was extracted and added to rule set. In this work, each individual in PSO algorithm includes a set of rules.…”

Section: Discussionmentioning

confidence: 99%

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Coronary Artery Disease Detection Using a Fuzzy-Boosting PSO Approach

Hedeshi

Abadeh

2014

Computational Intelligence and Neuroscience

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“…Usage of compressed ECG for fast and efficient telecardiology application is crucial, as ECG signals are enormously large in size (Sufi and Khalil, 2011) Hedeshi and Abadeh (2011) to extract a set of rules for diagnosis of coronary artery disease is presented. The boosting method considers the cooperation between fuzzy rules that generate with PSO meta-heuristic.…”

Section: Literature Reviewmentioning

confidence: 99%

Cardio Vascular Detection with Neuro Computing and Genetic Algorithm

Peter¹,

Somasundram²

2014

RJASET

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For human the most fundamental requirement is having a healthy life, which is being difficult to maintain day to day as we are getting more progress in technological era. Among the possible reasons of unnatural death, heart disease based causes are showing very significant part. The diagnosis of heart diseases is a vital and intricate job. The recognition of heart disease from diverse features or signs is a multi-layered problem that is highly sensitive with respect diagnostic tests and establishing the relationship with multiple parameters is very difficult. In result decision is not free from false assumptions and is frequently accompanied by impulsive effects. This encourages developing a more reliable and cost effective knowledge based algorithmic approach to detect the heart disease. From engineering point of view, solution for detecting the presence of heart diseases is developed with the concept of artificial intelligence in data mining in this study. Feed forward architecture of neural network technology is taken as platform of computation to generate the intelligence in association with well established field of genetic algorithm (GA). A comparative performance has presented between both learning concepts with various different size of architecture.

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