Download fulltext
HTML
Farshid, P., Mirnia, K., Rezaei-Hachesu, P., Maserat, E., & Samad-Soltani, T. (2023). Developing a model to predict neonatal respiratory distress syndrome and affecting factors using data mining: A cross-sectional study. International Journal of Reproductive BioMedicine (IJRM), 21(11), 909–920. https://doi.org/10.18502/ijrm.v21i11.14654
https://doi.org/10.18502/ijrm.v21i11.14654
statistics
- 191 Abstract Views
- 126 PDF Views
- 0 HTML Views
authors
-
Parisa Farshid
Parisa Farshid
Department of Health Information Technology, School of Management and Medical Informatics, Tabriz University of Medical Sciences, Tabriz, Iran.
-
Kayvan Mirnia
Kayvan Mirnia
Department of Pediatrics, Children’s Medical Center, Tehran University of Medical Sciences, Tehran, Iran.
-
Peyman Rezaei-Hachesu
Peyman Rezaei-Hachesu
Department of Health Information Technology, School of Management and Medical Informatics, Tabriz University of Medical Sciences, Tabriz, Iran.
-
Elham Maserat
Elham Maserat
Department of Medical Informatics, School of Medical Sciences, Tarbiat Modares University, Tehran, Iran.
-
Taha Samad-Soltani
Taha Samad-Soltani
Department of Health Information Technology, School of Management and Medical Informatics, Tabriz University of Medical Sciences, Tabriz, Iran.
Published Date
- Dec 14, 2023
Developing a model to predict neonatal respiratory distress syndrome and affecting factors using data mining: A cross-sectional study
Abstract
Background: One of the major challenges that hospitals and clinicians face is the early identification of newborns at risk for adverse events. One of them is neonatal respiratory distress syndrome (RDS). RDS is the widest spared respiratory disorder in immature newborns and the main source of death among them. Machine learning has been broadly accepted and used in various scopes to analyze medical information and is very useful in the early detection of RDS.
Objective: This study aimed to develop a model to predict neonatal RDS and affecting factors using data mining.
Materials and Methods: The original dataset in this cross-sectional study was extracted from the medical records of newborns diagnosed with RDS from July 2017-July 2018 in Alzahra hospital, Tabriz, Iran. This data includes information about 1469 neonates, and their mothers information. The data were preprocessed and applied to expand the classification model using machine learning techniques such as support vector machine, Naïve Bayes, classification tree, random forest, CN2 rule induction, and neural network, for prediction of RDS episodes. The study compares models according to their accuracy.
Results: Among the obtained results, an accuracy of 0.815, sensitivity of 0.802, specificity of 0.812, and area under the curve of 0.843 was the best output using random forest.
Conclusion: The findings of our study proved that new approaches, such as data mining, may support medical decisions, improving diagnosis in neonatal RDS. The feasibility of using a random forest in neonatal RDS prediction would offer the possibility to decrease postpartum complications of neonatal care.
Key words: Data mining, Classification, Neonatal respiratory distress syndrome, Newborn, Machine learning.
References
[1] Gallacher DJ, Hart K, Kotecha S. Common respiratory conditions of the newborn. Breathe 2016; 12: 30–42.
[2] Rimensberger PC. Pediatric and neonatal mechanical ventilation. Switzerland: Springer; 2014.
[3] Kumar MN, Koushik KVS, Deepak K. Prediction of heart diseases using data mining and machine learning algorithms and tools. Int J S Res CSE & IT 2018; 3: 44–51.
[4] Dai W, Brisimi TS, Adams WG, Mela T, Saligrama V, Paschalidis IC. Prediction of hospitalization due to heart diseases by supervised learning methods. Int J Med Informat 2015; 84: 189–197.
[5] Oliveria T, Barbosa E, Matins S, Goulart A, Neves J, Novais P. Aprognosis system for colorectal cancer. Proceedings of the 26th International Symposium on Computer Bases Medical Systems. 20-22 June 2013; Portugal.
[6] llayaraja M, Meyyappan T. Mining medical data to identify frequent diseases using Apriori algorithm. Proceeding of the International Conference on Pattern Recognition, Informatics and Mobile Engineering. 21-22 February 2013; India.
[7] Ferreira D, Oliveria A, Freitas A. Applying data mining techniques to improve diagnosis in neonatal jaundice. BMC Med Informat Decision Mak 2012; 12: 143.
[8] Daunhawer I, Kasser S, Koch G, Sieber L, Cakal H, Tütsch J, et al. Enhanced early prediction of clinically relevant neonatal hyperbilirubinemia with machine learning. Pediatr Res 2019; 86: 122–127.
[9] Precup D, Robles-Rubio CA, Brown KA, Kanbar L, Kaczmarek J, Chawla S, et al. Prediction of extubation readiness in extreme preterm infants based on measures of cardiorespiratory variability. Annu Int Conf IEEE Eng Med Biol Soc 2012; 2012: 5630–5633.
[10] 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: 1000118.
[11] Natarajan A, Lam G, Liu J, Beam AL, Beam KS, Levin JC. Prediction of extubation failure among low birthweight neonates using machine learning. J Perinatol 2023; 43: 209–214.
[12] Sheikhtaheri A, Zarkesh MR, Moradi R, Kermani F. Prediction of neonatal deaths in NICUs: Development and validation of machine learning models. BMC Med Inform Decis Mak 2021; 21: 131.
[13] Hajipour M, Taherpour N, Fateh H, Yousefi E, Etemad K, Zolfizadeh F, et al. Predictive factors of infant mortality using data mining in Iran. J Compr Ped 2021; 12: e108575.
[14] Najafian B, Saburi A, Fakhraei SH, Afjeh A, Eghbal F, Noroozian R. Predicting factors of INSURE failure in low birth-weight neonates with RDS: A logistic regression model. Iran J Neonatol 2015; 5: 30-34.
[15] Senthilkumar D, Paulraj S. Prediction of low birth weight infants and its risk factors using data mining techniques. Proceedings of the 2015 international conference on industrial engineering and operations management. 3-5 March 2015; Dubai.
[16] Hange U, Selvaraj R, Galani M, Letsholo K. A data-mining model for predicting low birth weight with a high AUC. In: Lee R. Computer and information Science. Cham: Springer; 2017.
[17] Ghaderi-Ghahfarokhi S, Sadeghifar J, Mozafari M. A model to predict low birth weight infants and affecting factors using data mining techniques. J Bas Res Med Sci 2018; 5: 1-8.
[18] Borson NS, Kabir MR, Zamal Z, Rahman RM. Correlation analysis of demographic factors on low birth weight and prediction modeling using machine learning techniques. 4th World Conference on Smart Trends in Systems, Security and Sustainability (WorldS4). October 2020; UK; 2020.
[19] Shirwaikar RD, Acharya UD, Makkithaya K, Surulivelrajan M, Lewis LES. Machine learning techniques for neonatal apnea prediction. J Artif Intell 2016; 9: 33–38.
[20] Morais A, Peixoto H, Coimbra C, Abelha A, Machado J. Predicting the need of neonatal resuscitation using data mining. Procedia Comput Sci 2017; 113: 571–576.
[21] Williamson JR, Bliss DW, Browne DW, Indic P, Bloch-Salisbury E, Paydarfar D. Individualized apnea prediction in preterm infants using cardio-respiratory and movement signals. 2013 IEEE International Conference on Body Sensor Networks. May 2013; USA; 2013.
[22] Shoshtarian Malak J, Zeraati H, Nayeri FS, Safdari R, Danesh Shahraki A. Neonatal intensive care decision support systems using artificial intelligence techniques: A systematic review. Artif Intell Rev 2019; 52: 2685-2704.
[23] Betts KS, Kisely S, Alati R. Predicting neonatal respiratory distress syndrome and hypoglycaemia prior to discharge: Leveraging health administrative data and machine learning. J Biomed Inform 2021; 114: 103651.
[24] Shearer C. The CRISP-DM model: The new blueprint for data mining. J Data Warehous 2000; 5: 22.
[25] Cios KJ, Moore GW. Uniqueness of medical data mining. Artif Intell Med 2002; 26: 1–24.
[26] Minale T, Mola M, Jemaneh G, Doyore F. Application of data mining techniques to predict urinary fistula surgical repair outcome: The case of Addis Ababa Fistula Hospital, Addis Ababa, Ethiopia. J Health Med Informat 2012; 5: 2.
[27] Tanner L, Schreiber M, Low JG, Ong A, Tolfvenstam T, Lai YL, et al. Decision tree algorithms predict the diagnosis and outcome of dengue fever in the early phase of illness. PLoS Negl Trop Dis 2008; 2: e196.
[28] Huang J, Ling ChX. Using AUC and accuracy in evaluating learning algorithms. IEEE Trans Knowl Data Eng 2005; 17: 299-310.
[29] Breiman L. Random forests. Machine Learning 2001; 45: 5–32.
[30] Bebortta S, Panda M, Panda SS. Classification of pathological disorders in children using random forest algorithm. 2020 International Conference on Emerging Trends in Information Technology and Engineering (ic-ETITE). February 2020; India.
[31] Safdari R, Kadivar M, Tabari P, Shawky Own H. [Comparision of data classification algorithms to determine type of jaundice in infants]. Payavard 2018; 11: 541–548. (in Persian)
[32] Chen ChK, Manlhiot C, Mital S, Schwartz SM, Van Arsdell GS, Caldarone Ch, et al. Prelisting predictions of early postoperative survival in infant heart transplantation using classification and regression tree analysis. Pediatr Transplant 2018; 22: e13105.
[33] Ferreira D, Oliveira A, Freitas A. Applying data mining techniques to improve diagnosis in neonatal jaundice. BMC Med Inform Decis Mak 2012; 12: 143.
[34] Mikhno A, Ennett CM. Prediction of extubation failure for neonates with respiratory distress syndrome using the MIMIC-II clinical database. Annu Int Conf IEEE Eng Med Biol Soc 2012; 2012: 5094–5097.
[2] Rimensberger PC. Pediatric and neonatal mechanical ventilation. Switzerland: Springer; 2014.
[3] Kumar MN, Koushik KVS, Deepak K. Prediction of heart diseases using data mining and machine learning algorithms and tools. Int J S Res CSE & IT 2018; 3: 44–51.
[4] Dai W, Brisimi TS, Adams WG, Mela T, Saligrama V, Paschalidis IC. Prediction of hospitalization due to heart diseases by supervised learning methods. Int J Med Informat 2015; 84: 189–197.
[5] Oliveria T, Barbosa E, Matins S, Goulart A, Neves J, Novais P. Aprognosis system for colorectal cancer. Proceedings of the 26th International Symposium on Computer Bases Medical Systems. 20-22 June 2013; Portugal.
[6] llayaraja M, Meyyappan T. Mining medical data to identify frequent diseases using Apriori algorithm. Proceeding of the International Conference on Pattern Recognition, Informatics and Mobile Engineering. 21-22 February 2013; India.
[7] Ferreira D, Oliveria A, Freitas A. Applying data mining techniques to improve diagnosis in neonatal jaundice. BMC Med Informat Decision Mak 2012; 12: 143.
[8] Daunhawer I, Kasser S, Koch G, Sieber L, Cakal H, Tütsch J, et al. Enhanced early prediction of clinically relevant neonatal hyperbilirubinemia with machine learning. Pediatr Res 2019; 86: 122–127.
[9] Precup D, Robles-Rubio CA, Brown KA, Kanbar L, Kaczmarek J, Chawla S, et al. Prediction of extubation readiness in extreme preterm infants based on measures of cardiorespiratory variability. Annu Int Conf IEEE Eng Med Biol Soc 2012; 2012: 5630–5633.
[10] 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: 1000118.
[11] Natarajan A, Lam G, Liu J, Beam AL, Beam KS, Levin JC. Prediction of extubation failure among low birthweight neonates using machine learning. J Perinatol 2023; 43: 209–214.
[12] Sheikhtaheri A, Zarkesh MR, Moradi R, Kermani F. Prediction of neonatal deaths in NICUs: Development and validation of machine learning models. BMC Med Inform Decis Mak 2021; 21: 131.
[13] Hajipour M, Taherpour N, Fateh H, Yousefi E, Etemad K, Zolfizadeh F, et al. Predictive factors of infant mortality using data mining in Iran. J Compr Ped 2021; 12: e108575.
[14] Najafian B, Saburi A, Fakhraei SH, Afjeh A, Eghbal F, Noroozian R. Predicting factors of INSURE failure in low birth-weight neonates with RDS: A logistic regression model. Iran J Neonatol 2015; 5: 30-34.
[15] Senthilkumar D, Paulraj S. Prediction of low birth weight infants and its risk factors using data mining techniques. Proceedings of the 2015 international conference on industrial engineering and operations management. 3-5 March 2015; Dubai.
[16] Hange U, Selvaraj R, Galani M, Letsholo K. A data-mining model for predicting low birth weight with a high AUC. In: Lee R. Computer and information Science. Cham: Springer; 2017.
[17] Ghaderi-Ghahfarokhi S, Sadeghifar J, Mozafari M. A model to predict low birth weight infants and affecting factors using data mining techniques. J Bas Res Med Sci 2018; 5: 1-8.
[18] Borson NS, Kabir MR, Zamal Z, Rahman RM. Correlation analysis of demographic factors on low birth weight and prediction modeling using machine learning techniques. 4th World Conference on Smart Trends in Systems, Security and Sustainability (WorldS4). October 2020; UK; 2020.
[19] Shirwaikar RD, Acharya UD, Makkithaya K, Surulivelrajan M, Lewis LES. Machine learning techniques for neonatal apnea prediction. J Artif Intell 2016; 9: 33–38.
[20] Morais A, Peixoto H, Coimbra C, Abelha A, Machado J. Predicting the need of neonatal resuscitation using data mining. Procedia Comput Sci 2017; 113: 571–576.
[21] Williamson JR, Bliss DW, Browne DW, Indic P, Bloch-Salisbury E, Paydarfar D. Individualized apnea prediction in preterm infants using cardio-respiratory and movement signals. 2013 IEEE International Conference on Body Sensor Networks. May 2013; USA; 2013.
[22] Shoshtarian Malak J, Zeraati H, Nayeri FS, Safdari R, Danesh Shahraki A. Neonatal intensive care decision support systems using artificial intelligence techniques: A systematic review. Artif Intell Rev 2019; 52: 2685-2704.
[23] Betts KS, Kisely S, Alati R. Predicting neonatal respiratory distress syndrome and hypoglycaemia prior to discharge: Leveraging health administrative data and machine learning. J Biomed Inform 2021; 114: 103651.
[24] Shearer C. The CRISP-DM model: The new blueprint for data mining. J Data Warehous 2000; 5: 22.
[25] Cios KJ, Moore GW. Uniqueness of medical data mining. Artif Intell Med 2002; 26: 1–24.
[26] Minale T, Mola M, Jemaneh G, Doyore F. Application of data mining techniques to predict urinary fistula surgical repair outcome: The case of Addis Ababa Fistula Hospital, Addis Ababa, Ethiopia. J Health Med Informat 2012; 5: 2.
[27] Tanner L, Schreiber M, Low JG, Ong A, Tolfvenstam T, Lai YL, et al. Decision tree algorithms predict the diagnosis and outcome of dengue fever in the early phase of illness. PLoS Negl Trop Dis 2008; 2: e196.
[28] Huang J, Ling ChX. Using AUC and accuracy in evaluating learning algorithms. IEEE Trans Knowl Data Eng 2005; 17: 299-310.
[29] Breiman L. Random forests. Machine Learning 2001; 45: 5–32.
[30] Bebortta S, Panda M, Panda SS. Classification of pathological disorders in children using random forest algorithm. 2020 International Conference on Emerging Trends in Information Technology and Engineering (ic-ETITE). February 2020; India.
[31] Safdari R, Kadivar M, Tabari P, Shawky Own H. [Comparision of data classification algorithms to determine type of jaundice in infants]. Payavard 2018; 11: 541–548. (in Persian)
[32] Chen ChK, Manlhiot C, Mital S, Schwartz SM, Van Arsdell GS, Caldarone Ch, et al. Prelisting predictions of early postoperative survival in infant heart transplantation using classification and regression tree analysis. Pediatr Transplant 2018; 22: e13105.
[33] Ferreira D, Oliveira A, Freitas A. Applying data mining techniques to improve diagnosis in neonatal jaundice. BMC Med Inform Decis Mak 2012; 12: 143.
[34] Mikhno A, Ennett CM. Prediction of extubation failure for neonates with respiratory distress syndrome using the MIMIC-II clinical database. Annu Int Conf IEEE Eng Med Biol Soc 2012; 2012: 5094–5097.