Using GBC Algorithm to Optimize Support Vector Machine Parameters for Predicting the Relationship between Cancer and Cardiac Infarction: A Case Study
Introduction: Awareness of cancer increases the probability of neurotic disorders and stress in the patient. Also, stress increases the risk of myocardial infarction. The present study aimed to determine the probability of a heart attack in cancer patients based on the GBC algorithm. Method: In this...
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| Format: | Article |
| Language: | fas |
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Kerman University of Medical Sciences
2018-12-01
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| Series: | مجله انفورماتیک سلامت و زیست پزشکی |
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| Online Access: | http://jhbmi.ir/article-1-312-en.html |
| _version_ | 1811176327911309312 |
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| author | Mehdi Nooshyar Mohammad Momeni Sorayya Gharravi Fatemeh Hourali |
| author_facet | Mehdi Nooshyar Mohammad Momeni Sorayya Gharravi Fatemeh Hourali |
| author_sort | Mehdi Nooshyar |
| collection | DOAJ |
| description | Introduction: Awareness of cancer increases the probability of neurotic disorders and stress in the patient. Also, stress increases the risk of myocardial infarction. The present study aimed to determine the probability of a heart attack in cancer patients based on the GBC algorithm.
Method: In this study, data were collected from the database of Shahid Sadoughi subspecialty hospital in Yazd. The medical records of 1679 patients with heart attack were studied, of which 81 ones belonged to patients with cancer. In the process of selecting features by the proposed model, if cancer is identified as an effective feature, then the relationship between cancer and cardiac infarction will be meaningful.
Results: Using the proposed model, the cancer feature was selected to predict the probability of heart attack, which indicated a significant relationship between these two characteristics in patients who were vulnerable to cardiac disease. The predictive accuracy of the proposed model was 0.91
Conclusion: By choosing the cancer feature, the proposed model compared to other models has the least error rate and the most accuracy in predicting myocardial infarction. Naive bias method has maximum error rate and minimum accuracy. The simulation results indicate that in patients who are vulnerable to cardiac disease, after being diagnosed with cancer during the early months, heart attack is possible. |
| first_indexed | 2024-04-10T19:49:56Z |
| format | Article |
| id | doaj.art-cd537a5915664227ad4f72ab4258b7a6 |
| institution | Directory Open Access Journal |
| issn | 2423-3870 2423-3498 |
| language | fas |
| last_indexed | 2024-04-10T19:49:56Z |
| publishDate | 2018-12-01 |
| publisher | Kerman University of Medical Sciences |
| record_format | Article |
| series | مجله انفورماتیک سلامت و زیست پزشکی |
| spelling | doaj.art-cd537a5915664227ad4f72ab4258b7a62023-01-28T10:41:53ZfasKerman University of Medical Sciencesمجله انفورماتیک سلامت و زیست پزشکی2423-38702423-34982018-12-0153361372Using GBC Algorithm to Optimize Support Vector Machine Parameters for Predicting the Relationship between Cancer and Cardiac Infarction: A Case StudyMehdi Nooshyar0Mohammad Momeni1Sorayya Gharravi2Fatemeh Hourali3 Ph.D., in Electrical Engineering, Associate Professor, Computer Dept., School of Electrical and Computer Engineering, University of Mohaghegh Ardabili, Ardabil, Iran Introduction: Awareness of cancer increases the probability of neurotic disorders and stress in the patient. Also, stress increases the risk of myocardial infarction. The present study aimed to determine the probability of a heart attack in cancer patients based on the GBC algorithm. Method: In this study, data were collected from the database of Shahid Sadoughi subspecialty hospital in Yazd. The medical records of 1679 patients with heart attack were studied, of which 81 ones belonged to patients with cancer. In the process of selecting features by the proposed model, if cancer is identified as an effective feature, then the relationship between cancer and cardiac infarction will be meaningful. Results: Using the proposed model, the cancer feature was selected to predict the probability of heart attack, which indicated a significant relationship between these two characteristics in patients who were vulnerable to cardiac disease. The predictive accuracy of the proposed model was 0.91 Conclusion: By choosing the cancer feature, the proposed model compared to other models has the least error rate and the most accuracy in predicting myocardial infarction. Naive bias method has maximum error rate and minimum accuracy. The simulation results indicate that in patients who are vulnerable to cardiac disease, after being diagnosed with cancer during the early months, heart attack is possible.http://jhbmi.ir/article-1-312-en.htmlcancerheart attackgbc algorithmsupport vector machineincrease precision of prediction |
| spellingShingle | Mehdi Nooshyar Mohammad Momeni Sorayya Gharravi Fatemeh Hourali Using GBC Algorithm to Optimize Support Vector Machine Parameters for Predicting the Relationship between Cancer and Cardiac Infarction: A Case Study مجله انفورماتیک سلامت و زیست پزشکی cancer heart attack gbc algorithm support vector machine increase precision of prediction |
| title | Using GBC Algorithm to Optimize Support Vector Machine Parameters for Predicting the Relationship between Cancer and Cardiac Infarction: A Case Study |
| title_full | Using GBC Algorithm to Optimize Support Vector Machine Parameters for Predicting the Relationship between Cancer and Cardiac Infarction: A Case Study |
| title_fullStr | Using GBC Algorithm to Optimize Support Vector Machine Parameters for Predicting the Relationship between Cancer and Cardiac Infarction: A Case Study |
| title_full_unstemmed | Using GBC Algorithm to Optimize Support Vector Machine Parameters for Predicting the Relationship between Cancer and Cardiac Infarction: A Case Study |
| title_short | Using GBC Algorithm to Optimize Support Vector Machine Parameters for Predicting the Relationship between Cancer and Cardiac Infarction: A Case Study |
| title_sort | using gbc algorithm to optimize support vector machine parameters for predicting the relationship between cancer and cardiac infarction a case study |
| topic | cancer heart attack gbc algorithm support vector machine increase precision of prediction |
| url | http://jhbmi.ir/article-1-312-en.html |
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