A Hybrid Model of Heart Anomalies Detection by Processing Heart Sounds
Introduction: Different factors are effective in detecting heart abnormalities. The greater the number of these factors, the greater the uncertainty in the detection of heart abnormalities. In the uncertainty condition in response of prediction model, the fuzzy systems are one of the most effective...
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| Format: | Article |
| Language: | fas |
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Kerman University of Medical Sciences
2019-09-01
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| Series: | مجله انفورماتیک سلامت و زیست پزشکی |
| Subjects: | |
| Online Access: | http://jhbmi.ir/article-1-278-en.html |
| _version_ | 1811176350293164032 |
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| author | Ehsan Aghaenjad Ramazan Taimourei-Yansary Ali Riahi |
| author_facet | Ehsan Aghaenjad Ramazan Taimourei-Yansary Ali Riahi |
| author_sort | Ehsan Aghaenjad |
| collection | DOAJ |
| description | Introduction: Different factors are effective in detecting heart abnormalities. The greater the number of these factors, the greater the uncertainty in the detection of heart abnormalities. In the uncertainty condition in response of prediction model, the fuzzy systems are one of the most effective methods for generating an acceptable response.
Method: In this applied study, 3240 records related to heart abnormalities were reviewed, each record contained heart sounds of healthy and unhealthy groups. Then, using fuzzy system, the rules of data for the input samples were extracted and the rules were used to categorize the heart abnormalities. Due to the dependency of the effective factors on heart abnormalities, many identical rules with a similar function that result in additional processing and reduced efficacy, will be produced. In the proposed method, the Hummingbird algorithm were used to choose the optimal output rules. Then, using the optimum output rules, the inputs data were categorized into normal and abnormal classes. Data were analyzed using the root mean squared error (RMSE) method.
Results: It was revealed that the mean accuracy and time of diagnosis of heart abnormalities in the proposed method were 99.6% and 0.56 seconds, respectively, indicating higher efficiency compared to the other similar studies.
Conclusion: Compared to the other methods, the proposed model provides more accurate diagnosis and classification. |
| first_indexed | 2024-04-10T19:50:05Z |
| format | Article |
| id | doaj.art-e493ae5418364c03be093d4da1644721 |
| institution | Directory Open Access Journal |
| issn | 2423-3870 2423-3498 |
| language | fas |
| last_indexed | 2024-04-10T19:50:05Z |
| publishDate | 2019-09-01 |
| publisher | Kerman University of Medical Sciences |
| record_format | Article |
| series | مجله انفورماتیک سلامت و زیست پزشکی |
| spelling | doaj.art-e493ae5418364c03be093d4da16447212023-01-28T10:31:13ZfasKerman University of Medical Sciencesمجله انفورماتیک سلامت و زیست پزشکی2423-38702423-34982019-09-0162101110A Hybrid Model of Heart Anomalies Detection by Processing Heart SoundsEhsan Aghaenjad0Ramazan Taimourei-Yansary1Ali Riahi2 MSc in Computer Engineering, Department of Computer Engineering, Bandargaz Branch, Islamic Azad University, Bandargaz, Iran Ph.D. in Computer Engineering Artificial Intelligence, Department of Computer Engineering, Bandargaz Branch, Islamic Azad University, Bandargaz, Iran Ph.D. in Computer Engineering- Software Systems, Department of Computer Engineering, Bandargaz Branch, Islamic Azad University, Bandargaz, Iran Introduction: Different factors are effective in detecting heart abnormalities. The greater the number of these factors, the greater the uncertainty in the detection of heart abnormalities. In the uncertainty condition in response of prediction model, the fuzzy systems are one of the most effective methods for generating an acceptable response. Method: In this applied study, 3240 records related to heart abnormalities were reviewed, each record contained heart sounds of healthy and unhealthy groups. Then, using fuzzy system, the rules of data for the input samples were extracted and the rules were used to categorize the heart abnormalities. Due to the dependency of the effective factors on heart abnormalities, many identical rules with a similar function that result in additional processing and reduced efficacy, will be produced. In the proposed method, the Hummingbird algorithm were used to choose the optimal output rules. Then, using the optimum output rules, the inputs data were categorized into normal and abnormal classes. Data were analyzed using the root mean squared error (RMSE) method. Results: It was revealed that the mean accuracy and time of diagnosis of heart abnormalities in the proposed method were 99.6% and 0.56 seconds, respectively, indicating higher efficiency compared to the other similar studies. Conclusion: Compared to the other methods, the proposed model provides more accurate diagnosis and classification.http://jhbmi.ir/article-1-278-en.htmlcardiac abnormalitiesheart rate processingfuzzy systemshummingbird algorithm |
| spellingShingle | Ehsan Aghaenjad Ramazan Taimourei-Yansary Ali Riahi A Hybrid Model of Heart Anomalies Detection by Processing Heart Sounds مجله انفورماتیک سلامت و زیست پزشکی cardiac abnormalities heart rate processing fuzzy systems hummingbird algorithm |
| title | A Hybrid Model of Heart Anomalies Detection by Processing Heart Sounds |
| title_full | A Hybrid Model of Heart Anomalies Detection by Processing Heart Sounds |
| title_fullStr | A Hybrid Model of Heart Anomalies Detection by Processing Heart Sounds |
| title_full_unstemmed | A Hybrid Model of Heart Anomalies Detection by Processing Heart Sounds |
| title_short | A Hybrid Model of Heart Anomalies Detection by Processing Heart Sounds |
| title_sort | hybrid model of heart anomalies detection by processing heart sounds |
| topic | cardiac abnormalities heart rate processing fuzzy systems hummingbird algorithm |
| url | http://jhbmi.ir/article-1-278-en.html |
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