| ORIGINAL ARTICLE |
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| Year : 2023 | Volume
: 12
| Issue : 1 | Page : 130 |
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Identifying the factors affecting the incidence of congenital heart disease using support vector machine and particle swarm optimization
Bahar Dehghan1, Mohammad Reza Sabri1, Alireza Ahmadi1, Mehdi Ghaderian1, Chehreh Mahdavi1, Davood Ramezani Nejad1, Mohammad Sattari2
1 Pediatric Cardiovascular Research Center, Cardiovascular Research Institute, Isfahan University of Medical Sciences, Isfahan, Iran
2 Health Information Technology Research Center, Isfahan University of Medical Sciences, Isfahan, Iran
Correspondence Address:
Dr. Mohammad Sattari
Department of Health Information Technology and Management, School of Medical Management and Information Sciences, Isfahan University of Medical Sciences, Hezarjerib Avenue, P.O. Box: 81745.346, Isfahan
Iran
 Source of Support: None, Conflict of Interest: None  | Check |
DOI: 10.4103/abr.abr_54_22
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Background: Congenital malformations are defined as “any defect in the structure of a person that exists from birth”. Among them, congenital heart malformations have the highest prevalence in the world. This study focuses on the development of a predictive model for congenital heart disease in Isfahan using support vector machine (SVM) and particle swarm intelligence.
Materials and Methods: It consists of four parts: data collection, preprocessing, identify target features, and technique. The proposed technique is a combination of the SVM method and particle swarm optimization (PSO).
Results: The data set includes 1389 patients and 399 features. The best performance in terms of accuracy, with 81.57%, is related to the PSO-SVM technique and the worst performance, with 78.62%, is related to the random forest technique. Congenital extra cardiac anomalies are considered as the most important factor with averages of 0.655.
Conclusion: Congenital extra cardiac anomalies are considered as the most important factor. Detecting more important feature affecting congenital heart disease allows physicians to treat the variable risk factors associated with congenital heart disease progression. The use of a machine learning approach provides the ability to predict the presence of congenital heart disease with high accuracy and sensitivity.
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