Volume 25, Issue 4 (3-2023)
jha 2023, 25(4): 28-44 |
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Jafari T, Nasiri S, Sayadi M, Emami H, Mohammadpour S. A Neonatal jaundice prediction system based on the support vector machine algorithm. jha 2023; 25 (4) :28-44
URL: http://jha.iums.ac.ir/article-1-4221-en.html
URL: http://jha.iums.ac.ir/article-1-4221-en.html
1- M.Sc, School of Health Management and Information Sciences, Iran University of Medical Sciences, Tehran
2- Assistant Professor, Department of Health Information Management, School of Health Management and Information Sciences, Iran University of Medical Sciences, Tehran
3- Ph.D Student, School of Health Management and Information Sciences, Iran University of Medical Sciences, Tehran
4- Ph.D Student, Department of Health Information Management and Technology, School of Allied Medical Sciences, Shahid Beheshti University of Medical Sciences, Tehran ,samanmohammadpour90@gmail.com
2- Assistant Professor, Department of Health Information Management, School of Health Management and Information Sciences, Iran University of Medical Sciences, Tehran
3- Ph.D Student, School of Health Management and Information Sciences, Iran University of Medical Sciences, Tehran
4- Ph.D Student, Department of Health Information Management and Technology, School of Allied Medical Sciences, Shahid Beheshti University of Medical Sciences, Tehran ,
Abstract: (1019 Views)
Introduction: Jaundice is one of the most common problems in the neonatal period, affecting about 60% of full-term and 80% of premature infants in their first week of life. The present study aimed to develop a system for predicting neonatal jaundice within the first 24 to 72 hours post-delivery by using the Support Vector Machine (SVM) algorithm.
Methods: This applied-developmental study employed a quantitative method. First, based on a literature review, a questionnaire containing effective factors for predicting jaundice in newborns was designed. Data analysis was performed using descriptive statistics, and factors that were recognized as necessary by at least 50% of the experts were included in the model. Then, data from 1178 newborns delivered at Lolagar hospital in Tehran were extracted from birth records, and several machine learning algorithms were used to predict neonatal jaundice.
Results: The findings of this research showed that the proposed model based on the SVM algorithm is the best output due to the distance between classes. Therefore, the final model of the SVM algorithm was created using the Gaussian kernel, with a sigma value of 1.2360605. Thirty percent of the samples (354 cases) were tested, and 321 cases were correctly predicted. In the proposed SVM model, parameters such as precision, the area under the Receiver Operating Characteristic (ROC), and F1 score were 92.7%, 93%, and 88% respectively.
Conclusion: Incorporating SVM into a system for predicting jaundice in newborns can aid doctors with timely prediction of jaundice in newborns and provide the possibility of taking preventive measures and preventing possible risks caused by jaundice in newborns.
Methods: This applied-developmental study employed a quantitative method. First, based on a literature review, a questionnaire containing effective factors for predicting jaundice in newborns was designed. Data analysis was performed using descriptive statistics, and factors that were recognized as necessary by at least 50% of the experts were included in the model. Then, data from 1178 newborns delivered at Lolagar hospital in Tehran were extracted from birth records, and several machine learning algorithms were used to predict neonatal jaundice.
Results: The findings of this research showed that the proposed model based on the SVM algorithm is the best output due to the distance between classes. Therefore, the final model of the SVM algorithm was created using the Gaussian kernel, with a sigma value of 1.2360605. Thirty percent of the samples (354 cases) were tested, and 321 cases were correctly predicted. In the proposed SVM model, parameters such as precision, the area under the Receiver Operating Characteristic (ROC), and F1 score were 92.7%, 93%, and 88% respectively.
Conclusion: Incorporating SVM into a system for predicting jaundice in newborns can aid doctors with timely prediction of jaundice in newborns and provide the possibility of taking preventive measures and preventing possible risks caused by jaundice in newborns.
Type of Study: Research |
Subject:
Health Information Management
Received: 2022/09/13 | Accepted: 2022/12/19 | Published: 2023/03/24
Received: 2022/09/13 | Accepted: 2022/12/19 | Published: 2023/03/24
References
1. Ferreira D, Oliveira A, Freitas A. Applying data mining techniques to improve diagnosis in neonatal jaundice. BMC Med Inform Decis Mak. 2012;12:1-6. [DOI:10.1186/1472-6947-12-143]
2. Nelson WE, Kliegman R. Nelson textbook of pediatrics. 19th ed. Philadelphia: Elsevier Saunders; 2011.
3. Onyearugha CN, Chapp-Jumbo A, George IO. Neonatal jaundice: Evaluating the knowledge and practice of expectant mothers in aba, Nigeria. Journal of Health Science Research. 2016;1(2):42-7. [DOI:10.18311/jhsr/2016/v1/i2/4918]
4. Mansor MN, Yaacob S, Muthusamy H, Nisha Basah S, Ahmad Jamil SHFS, Mohd Khidir ML, et al. PCA-based feature extraction and K-NN algorithm for early jaundice detection. International Journal of Soft Computing and Software Engineering. 2011;1(1):25-9.
5. Maisels MJ, Bhutani VK, Bogen D, Newman TB, Stark AR, Watchko JF. Hyperbilirubinemia in the newborn infant≥ 35 weeks' gestation: An update with clarifications. Pediatrics. 2009;124(4):1193-8. [DOI:10.1542/peds.2009-0329]
6. Bhutani VK. Phototherapy to prevent severe neonatal hyperbilirubinemia in the newborn infant 35 or more weeks of gestation. Pediatrics. 2011;128(4):1-7. [DOI:10.1542/peds.2011-1494]
7. Wong RJ, Bhutani VK. Pathogenesis and etiology of unconjugated hyperbilirubinemia in the newborn. 2014.
8. Schwartz HP, Haberman BE, Ruddy RM. Hyperbilirubinemia: Current guidelines and emerging therapies. Pediatr Emerg Care. 2011;27(9):884-9. [DOI:10.1097/PEC.0b013e31822c9b4c]
9. Boskabadi H, Zakeri Hamidi M, Goudarzi M. Investigating the effect of maternal risk factors in incidence of neonatal jaundice. Iranian journal of obstetrics, gynecology, and infertility. 2013;15(34):1-6. [In Persian]
10. Najib Kh, Saki F, Hemmati F, Inaloo S. Incidence, risk factors and causes of severe neonatal hyperbilirubinemia in the South of Iran (Fars province). Iran Red Crescent Med J. 2013;15(3):260-3. [In Persian] [DOI:10.5812/ircmj.3337]
11. Mansor MN, Yaacob S, Hariharan M, Basah SN, Ahmad Jamil SHFS, Mohd Khidir ML, et al. Jaundice in newborn monitoring using color detection method. Procedia Eng. 2012;29:1631-5. [DOI:10.1016/j.proeng.2012.01.185]
12. Shukla M, Agarwal M. Knowledge of mothers regarding neonatal jaundice attending immunisation clinic at a tertiary care hospital of Lucknow. International Journal of Applied Research. 2016;2(6):297-9.
13. Arulmozhi A, Ezhilarasi M. Maximal information compression index (MICI) and PSO based detection of jaundice. Journal of Multiple-Valued Logic and Soft Computing. 2015;24(5-6):583-97.
14. Ramachandran A. Neonatal hyperbilirubinaemia. Paediatr Child Health. 2016;26(4):162-8. [DOI:10.1016/j.paed.2015.12.002]
15. Lauer BJ, Spector ND. Hyperbilirubinemia in the newborn. Pediatr Rev. 2011;32(8):341-9. [DOI:10.1542/pir.32.8.341]
16. Calado CS, Pereira AG, Santos VN, Castro MJ, Maio JF. What brings newborns to the emergency department? A 1-year study. Pediatr Emerg Care. 2009;25(4):244-8. [DOI:10.1097/PEC.0b013e31819e361d]
17. Safdari R, Kadivar M, Tabari P, Shawky Own H. Comparison of data classification algorithms to determine the type of neonatal jaundice. Payavard Salamat. 2018;11(5):541-8. [In Persian]
18. Taylor RA, Pare JR, Venkatesh AK, Mowafi H, Melnick ER, Fleischman W, et al. Prediction of in‐hospital mortality in emergency department patients with sepsis: A local big data driven, machine learning approach. Acad Emerg Med. 2016;23(3):269-78. [DOI:10.1111/acem.12876]
19. Sayadi M, Varadarajan V, Sadoughi F, Chopannejad S, Langarizadeh M. A machine learning model for detection of coronary artery disease using noninvasive clinical parameters. Life. 2022;12(11):1-10. [DOI:10.3390/life12111933]
20. Mohammadzadeh N, mosayebi Z, Beigy H, Shojaeinia M. Prediction of sepsis due to acinetobacter infection in neonates admitted to nicu. Payavard Salamat. 2021;14(6):497-505. [In Persian]
21. Mueller M, Almeida JS, Stanislaus R, Wagner CL. Can machine learning methods predict extubation outcome in premature infants as well as clinicians? J Neonatal Biol. 2013;2:1-18. [DOI:10.1109/IJCNN.2013.6707058]
22. Rezaee Kh, Haddadnia J, Rasegh Ghezelbash M. A novel algorithm for accurate diagnosis of hepatitis B and its severity. International Journal of Hospital Research. 2014;3(1):1-10.
23. Pal M, Foody GM. Feature selection for classification of hyperspectral data by SVM. IEEE Trans Geosci Rem Sens. 2010;48(5):2297-307. [DOI:10.1109/TGRS.2009.2039484]
24. Dormohammadi S, Alizadeh S, Asghari M, Shami M. Proposing a prediction model for diagnosing causes of infertility by data mining algorithms. Journal of Health Administration. 2014;17(57):46-57. [In Persian]
25. Farajollahi B. Presenting of prediction model for successful allogenic hematopoietic stem cell transplantation in adults with acute myeloid leukemia [master's thesis]. Theran: Iran University of Medical Sciences; 2022. [In Persian]
26. Hashemian AH, Manochehri S, Afshari D, Manochehri Z, Salari N, Shahsavari S. Prognosis of multiple sclerosis disease using data mining approaches random forest and support vector machine based on genetic algorithm. Tehran University Medical Journal. 2019;77(1):33-40. [In Persian]
27. Setareh S, Zahiri M, Zare Bandamiri M, Raeesi A, Abbasi R. Using data mining for survival prediction in patients with colon cancer. Iranian Journal of Epidemiology. 2018;14(1):19-29. [In Persian] [DOI:10.1155/2018/9678097]
28. Cruz JA, Wishart DS. Applications of machine learning in cancer prediction and prognosis. Cancer Inform. 2006;2:59-77. [DOI:10.1177/117693510600200030]
29. Castro-Ramos J, Toxqui-Quitl C, Villa Manriquez F, Orozco-Guillen E, Padilla-Vivanco A, Sanchez-Escobar J. Detecting jaundice by using digital image processing. Proceedings of SPIE. 2014;8949:1-8. [DOI:10.1117/12.2041354]
30. Adebayo Idowu P, Egejuru NC, Ademola Balogun J, Ajibola Sarumi O. Comparative analysis of prognostic model for risk classification of neonatal jaundice using machine learning algorithms. Computer Reviews Journal. 2019;3:122-46.
31. Umer A. Early prediction of neonatal jaundice using machine learning [master's thesis]. Ethiopia: Bahir Dar University; 2021.
32. Sussma S, Srivignesh S, Kishore VS, Marimuthu M. Jaundice prediction using machine learning approach. International Journal of Advance Research, Ideas and Innovations in Technology. 2022;7(6):493-5.
33. Firouzi Jahantigh F, Nazarnejad R, Firouzi Jahantigh M. Investigating the risk factors for low birth weight using data mining: A case study of Imam Ali hospital, Zahedan, Iran. Journal of Mazandaran University of Medical Sciences. 2016;25(133):171-82. [In Persian]
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