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Year 2025, Volume: 8 Issue: 2, 301 - 311, 30.06.2025

Hatice Kabaoğlu , Fecir Duran , Emine Uçar

https://doi.org/10.35377/saucis...1612668

Abstract

References

  • Goldenberg, R. L., & Rouse, D. J. (1998). Prevention of premature birth. New England Journal of Medicine, 339(5), 313-320.
  • Kumar, V., Shearer, J. C., Kumar, A., & Darmstadt, G. L. (2009). Neonatal hypothermia in low resource settings: a review. Journal of perinatology, 29(6), 401-412
  • Lunze, K., & Hamer, D. H. (2012). Thermal protection of the newborn in resource-limited environments. Journal of Perinatology, 32(5), 317-324.
  • H. Elayan, M. Aloqaily, and M. Guizani, “Digital Twin for Intelligent Context-Aware IoT Healthcare Systems,” IEEE Internet of Things Journal, vol. 8, no. 23, pp. 16749–16757, Dec. 2021, doi: 10.1109/JIOT.2021.3051158.
  • Y. Liu et al., "A Novel Cloud-Based Framework for the Elderly Healthcare Services Using Digital Twin," in IEEE Access, vol. 7, pp. 49088-49101, 2019, doi: 10.1109/ACCESS.2019.2909828
  • Erol, T., Mendi, A. F., & Doğan, D. (2020, October). The digital twin revolution in healthcare. In 2020 4th international symposium on multidisciplinary studies and innovative technologies (ISMSIT) (pp. 1-7). IEEE.
  • A. Haleem, M. Javaid, R. Pratap Singh, and R. Suman, “Exploring the revolution in healthcare systems through the applications of digital twin technology,” Biomedical Technology, vol. 4, pp. 28–38, Dec. 2023, doi: 10.1016/j.bmt.2023.02.001.
  • M. Peshkova, V. Yumasheva, E. Rudenko, N. Kretova, P. Timashev, and T. Demura, “Digital twin concept: Healthcare, education, research,” Journal of Pathology Informatics, vol. 14, p. 100313, Jan. 2023, doi: 10.1016/j.jpi.2023.100313.
  • Y. Han, Y. Li, Y. Li, B. Yang, and L. Cao, “Digital twinning for smart hospital operations: Framework and proof of concept,” Technology in Society, vol. 74, p. 102317, Aug. 2023, doi: 10.1016/j.techsoc.2023.102317.
  • “A digital twin framework for prognostics and health management,” Computers in Industry, vol. 150, p. 103948, Sep. 2023, doi: 10.1016/j.compind.2023.103948.
  • R. Aluvalu, S. Mudrakola, U. M. V, A. C. Kaladevi, M. V. S. Sandhya, and C. R. Bhat, “The novel emergency hospital services for patients using digital twins,” Microprocessors and Microsystems, vol. 98, p. 104794, Apr. 2023, doi: 10.1016/j.micpro.2023.104794.
  • M. J. Kaur, V. P. Mishra, and P. Maheshwari, “The convergence of digital twin, IoT, and machine learning: transforming data into action,” Digital twin technologies and smart cities, pp. 3–17, 2020.
  • J. Kumari, R. Karim, K. Karim, and M. Arenbro, “MetaAnalyser - A Concept and Toolkit for Enablement of Digital Twin,” IFAC-PapersOnLine, vol. 55, no. 2, pp. 199–204, Jan. 2022, doi: 10.1016/j.ifacol.2022.04.193.
  • I. Kononenko and M. Kukar, Machine learning and data mining. Horwood Publishing, 2007.
  • Attaran, M., & Celik, B. G. (2023). Digital twin: Benefits, use cases, challenges, and opportunities. Decision Analytics Journal, 6, 100165. https://doi.org/10.1016/j.dajour.2023.100165
  • Dassault Systèmes. (n.d.). The Living Heart Project. https://www.3ds.com/products-services/simulia/solutions/life-sciences-healthcare/the-living-heart-project/
  • A. Manocha, Y. Afaq, and M. Bhatia, “Digital Twin-assisted Blockchain-inspired irregular event analysis for eldercare,” Knowledge-Based Systems, vol. 260, p. 110138, Jan. 2023, doi: 10.1016/j.knosys.2022.110138.
  • Wahab, S. M. A. A., & Saad, M. (2022). Digital transformation acceleration in health sector during COVID-19: Drivers and consequences. Journal of Business and Management Sciences, 10(4), 164-179.
  • C. Quilodrán-Casas, V. L. S. Silva, R. Arcucci, C. E. Heaney, Y. Guo, and C. C. Pain, “Digital twins based on bidirectional LSTM and GAN for modelling the COVID-19 pandemic,” Neurocomputing, vol. 470, pp. 11–28, Jan. 2022, doi: 10.1016/j.neucom.2021.10.043.
  • D. Chen, N. A. AlNajem, and M. Shorfuzzaman, “Digital twins to fight against COVID-19 pandemic,” Internet of Things and Cyber-Physical Systems, vol. 2, pp. 70–81, Jan. 2022, doi: 10.1016/j.iotcps.2022.05.003.
  • Q. Lv, R. Zhang, X. Sun, Y. Lu, and J. Bao, “A digital twin-driven human-robot collaborative assembly approach in the wake of COVID-19,” Journal of Manufacturing Systems, vol. 60, pp. 837–851, Jul. 2021, doi: 10.1016/j.jmsy.2021.02.011.
  • H. Neog, P. E. Dutta, and N. Medhi, “Health condition prediction and covid risk detection using healthcare 4.0 techniques,” Smart Health, vol. 26, p. 100322, Dec. 2022, doi: 10.1016/j.smhl.2022.100322.
  • X. Shi, Z. Chen, H. Wang, D.-Y. Yeung, W.-K. Wong, and W. Woo, “Convolutional LSTM network: A machine learning approach for precipitation nowcasting,” Advances in neural information processing systems, vol. 28, 2015.
  • S. Hochreiter and J. Schmidhuber, “Long Short-Term Memory,” Neural Computation, vol. 9, no. 8, pp. 1735–1780, Nov. 1997, doi: 10.1162/neco.1997.9.8.1735.
  • C. Quilodrán-Casas, R. Arcucci, C. Pain, and Y. Guo, “Adversarially trained LSTMs on reduced order models of urban air pollution simulations,” arXiv preprint arXiv:2101.01568, 2021.
  • A. Elsheikh, S. Yacout, and M.-S. Ouali, “Bidirectional handshaking LSTM for remaining useful life prediction,” Neurocomputing, vol. 323, pp. 148–156, Jan. 2019, doi: 10.1016/j.neucom.2018.09.076.
  • G. Liu and J. Guo, “Bidirectional LSTM with attention mechanism and convolutional layer for text classification,” Neurocomputing, vol. 337, pp. 325–338, Apr. 2019, doi: 10.1016/j.neucom.2019.01.078.
  • O. Yeler and M. F. Koseoglu, “Performance prediction modeling of a premature baby incubator having modular thermoelectric heat pump system,” Applied Thermal Engineering, vol. 182, p. 116036, 2021.
  • R. Cuervo, M. A. Rodríguez-Lázaro, R. Farré, D. Gozal, G. Solana, and J. Otero, “Low-cost and open-source neonatal incubator operated by an Arduino microcontroller,” HardwareX, vol. 15, p. e00457, Sep. 2023, doi: 10.1016/j.ohx.2023.e00457.
  • P. T. Kapen, Y. Mohamadou, F. Momo, D. K. Jauspin, N. Kanmagne, and D. D. Jordan, “Development of a neonatal incubator with phototherapy, biometric fingerprint reader, remote monitoring, and heart rate control adapted for developing countries hospitals,” Journal of Neonatal Nursing, vol. 25, no. 6, pp. 298–303, Dec. 2019, doi: 10.1016/j.jnn.2019.07.011.
  • A. Hannouch, T. Lemenand, K. Khoury, and C. Habchi, “Heat and Mass Transfer of Preterm Neonates Nursed inside Incubators-A Review,” Thermal Science and Engineering Progress, p. 100553, 2020.
  • V. Puyana-Romero et al., “Reverberation time measurements of a neonatal incubator,” Applied Acoustics, vol. 167, p. 107374, 2020.
  • A. Chandrasekaran et al., “Disposable low-cost cardboard incubator for thermoregulation of stable preterm infant – a randomized controlled non-inferiority trial,” EClinicalMedicine, vol. 31, p. 100664, Jan. 2021, doi: 10.1016/j.eclinm.2020.100664.
  • A. Fraguela, F. D. Matlalcuatzi, and Á. M. Ramos, “Mathematical modelling of thermoregulation processes for premature infants in closed convectively heated incubators,” Computers in biology and medicine, vol. 57, pp. 159–172, 2015.

The Development of Digital Twin Baby Incubators for Fault Detection and Performance Analysis

Year 2025, Volume: 8 Issue: 2, 301 - 311, 30.06.2025

Hatice Kabaoğlu , Fecir Duran , Emine Uçar

https://doi.org/10.35377/saucis...1612668

Abstract

This study focuses on developing a digital twin for baby incubators in neonatal intensive care units to enhance monitoring and care for premature infants. The digital twin employs a hybrid model integrating Long Short-Term Memory (LSTM) and Random Forest (RF) algorithms to predict potential errors and alarms. The LSTM algorithm was trained using sensor data provided by a health technology company to predict future measurements. Subsequently, the RF algorithm classifies these predictions into specific error conditions. The hybrid model demonstrates success with mean squared error and mean absolute error values of 1540533.6 and 160.8 for the LSTM model and an 86.44% accuracy rate for the RF model. The study's key findings emphasize the effectiveness of the hybrid model in predicting future sensor values and classifying errors, representing a significant step towards improving premature baby care. Integrating LSTM and RF algorithms offers an innovative approach to error prediction, minimizing risks and improving premature infant health outcomes. In summary, this study successfully develops a digital twin for baby incubators, offering a promising solution for advancing newborn healthcare services and providing a foundation for future research.

Keywords

Baby Incubator Digital Twin Decision Tree Health Monitoring Machine Learning Classifier

References

  • Goldenberg, R. L., & Rouse, D. J. (1998). Prevention of premature birth. New England Journal of Medicine, 339(5), 313-320.
  • Kumar, V., Shearer, J. C., Kumar, A., & Darmstadt, G. L. (2009). Neonatal hypothermia in low resource settings: a review. Journal of perinatology, 29(6), 401-412
  • Lunze, K., & Hamer, D. H. (2012). Thermal protection of the newborn in resource-limited environments. Journal of Perinatology, 32(5), 317-324.
  • H. Elayan, M. Aloqaily, and M. Guizani, “Digital Twin for Intelligent Context-Aware IoT Healthcare Systems,” IEEE Internet of Things Journal, vol. 8, no. 23, pp. 16749–16757, Dec. 2021, doi: 10.1109/JIOT.2021.3051158.
  • Y. Liu et al., "A Novel Cloud-Based Framework for the Elderly Healthcare Services Using Digital Twin," in IEEE Access, vol. 7, pp. 49088-49101, 2019, doi: 10.1109/ACCESS.2019.2909828
  • Erol, T., Mendi, A. F., & Doğan, D. (2020, October). The digital twin revolution in healthcare. In 2020 4th international symposium on multidisciplinary studies and innovative technologies (ISMSIT) (pp. 1-7). IEEE.
  • A. Haleem, M. Javaid, R. Pratap Singh, and R. Suman, “Exploring the revolution in healthcare systems through the applications of digital twin technology,” Biomedical Technology, vol. 4, pp. 28–38, Dec. 2023, doi: 10.1016/j.bmt.2023.02.001.
  • M. Peshkova, V. Yumasheva, E. Rudenko, N. Kretova, P. Timashev, and T. Demura, “Digital twin concept: Healthcare, education, research,” Journal of Pathology Informatics, vol. 14, p. 100313, Jan. 2023, doi: 10.1016/j.jpi.2023.100313.
  • Y. Han, Y. Li, Y. Li, B. Yang, and L. Cao, “Digital twinning for smart hospital operations: Framework and proof of concept,” Technology in Society, vol. 74, p. 102317, Aug. 2023, doi: 10.1016/j.techsoc.2023.102317.
  • “A digital twin framework for prognostics and health management,” Computers in Industry, vol. 150, p. 103948, Sep. 2023, doi: 10.1016/j.compind.2023.103948.
  • R. Aluvalu, S. Mudrakola, U. M. V, A. C. Kaladevi, M. V. S. Sandhya, and C. R. Bhat, “The novel emergency hospital services for patients using digital twins,” Microprocessors and Microsystems, vol. 98, p. 104794, Apr. 2023, doi: 10.1016/j.micpro.2023.104794.
  • M. J. Kaur, V. P. Mishra, and P. Maheshwari, “The convergence of digital twin, IoT, and machine learning: transforming data into action,” Digital twin technologies and smart cities, pp. 3–17, 2020.
  • J. Kumari, R. Karim, K. Karim, and M. Arenbro, “MetaAnalyser - A Concept and Toolkit for Enablement of Digital Twin,” IFAC-PapersOnLine, vol. 55, no. 2, pp. 199–204, Jan. 2022, doi: 10.1016/j.ifacol.2022.04.193.
  • I. Kononenko and M. Kukar, Machine learning and data mining. Horwood Publishing, 2007.
  • Attaran, M., & Celik, B. G. (2023). Digital twin: Benefits, use cases, challenges, and opportunities. Decision Analytics Journal, 6, 100165. https://doi.org/10.1016/j.dajour.2023.100165
  • Dassault Systèmes. (n.d.). The Living Heart Project. https://www.3ds.com/products-services/simulia/solutions/life-sciences-healthcare/the-living-heart-project/
  • A. Manocha, Y. Afaq, and M. Bhatia, “Digital Twin-assisted Blockchain-inspired irregular event analysis for eldercare,” Knowledge-Based Systems, vol. 260, p. 110138, Jan. 2023, doi: 10.1016/j.knosys.2022.110138.
  • Wahab, S. M. A. A., & Saad, M. (2022). Digital transformation acceleration in health sector during COVID-19: Drivers and consequences. Journal of Business and Management Sciences, 10(4), 164-179.
  • C. Quilodrán-Casas, V. L. S. Silva, R. Arcucci, C. E. Heaney, Y. Guo, and C. C. Pain, “Digital twins based on bidirectional LSTM and GAN for modelling the COVID-19 pandemic,” Neurocomputing, vol. 470, pp. 11–28, Jan. 2022, doi: 10.1016/j.neucom.2021.10.043.
  • D. Chen, N. A. AlNajem, and M. Shorfuzzaman, “Digital twins to fight against COVID-19 pandemic,” Internet of Things and Cyber-Physical Systems, vol. 2, pp. 70–81, Jan. 2022, doi: 10.1016/j.iotcps.2022.05.003.
  • Q. Lv, R. Zhang, X. Sun, Y. Lu, and J. Bao, “A digital twin-driven human-robot collaborative assembly approach in the wake of COVID-19,” Journal of Manufacturing Systems, vol. 60, pp. 837–851, Jul. 2021, doi: 10.1016/j.jmsy.2021.02.011.
  • H. Neog, P. E. Dutta, and N. Medhi, “Health condition prediction and covid risk detection using healthcare 4.0 techniques,” Smart Health, vol. 26, p. 100322, Dec. 2022, doi: 10.1016/j.smhl.2022.100322.
  • X. Shi, Z. Chen, H. Wang, D.-Y. Yeung, W.-K. Wong, and W. Woo, “Convolutional LSTM network: A machine learning approach for precipitation nowcasting,” Advances in neural information processing systems, vol. 28, 2015.
  • S. Hochreiter and J. Schmidhuber, “Long Short-Term Memory,” Neural Computation, vol. 9, no. 8, pp. 1735–1780, Nov. 1997, doi: 10.1162/neco.1997.9.8.1735.
  • C. Quilodrán-Casas, R. Arcucci, C. Pain, and Y. Guo, “Adversarially trained LSTMs on reduced order models of urban air pollution simulations,” arXiv preprint arXiv:2101.01568, 2021.
  • A. Elsheikh, S. Yacout, and M.-S. Ouali, “Bidirectional handshaking LSTM for remaining useful life prediction,” Neurocomputing, vol. 323, pp. 148–156, Jan. 2019, doi: 10.1016/j.neucom.2018.09.076.
  • G. Liu and J. Guo, “Bidirectional LSTM with attention mechanism and convolutional layer for text classification,” Neurocomputing, vol. 337, pp. 325–338, Apr. 2019, doi: 10.1016/j.neucom.2019.01.078.
  • O. Yeler and M. F. Koseoglu, “Performance prediction modeling of a premature baby incubator having modular thermoelectric heat pump system,” Applied Thermal Engineering, vol. 182, p. 116036, 2021.
  • R. Cuervo, M. A. Rodríguez-Lázaro, R. Farré, D. Gozal, G. Solana, and J. Otero, “Low-cost and open-source neonatal incubator operated by an Arduino microcontroller,” HardwareX, vol. 15, p. e00457, Sep. 2023, doi: 10.1016/j.ohx.2023.e00457.
  • P. T. Kapen, Y. Mohamadou, F. Momo, D. K. Jauspin, N. Kanmagne, and D. D. Jordan, “Development of a neonatal incubator with phototherapy, biometric fingerprint reader, remote monitoring, and heart rate control adapted for developing countries hospitals,” Journal of Neonatal Nursing, vol. 25, no. 6, pp. 298–303, Dec. 2019, doi: 10.1016/j.jnn.2019.07.011.
  • A. Hannouch, T. Lemenand, K. Khoury, and C. Habchi, “Heat and Mass Transfer of Preterm Neonates Nursed inside Incubators-A Review,” Thermal Science and Engineering Progress, p. 100553, 2020.
  • V. Puyana-Romero et al., “Reverberation time measurements of a neonatal incubator,” Applied Acoustics, vol. 167, p. 107374, 2020.
  • A. Chandrasekaran et al., “Disposable low-cost cardboard incubator for thermoregulation of stable preterm infant – a randomized controlled non-inferiority trial,” EClinicalMedicine, vol. 31, p. 100664, Jan. 2021, doi: 10.1016/j.eclinm.2020.100664.
  • A. Fraguela, F. D. Matlalcuatzi, and Á. M. Ramos, “Mathematical modelling of thermoregulation processes for premature infants in closed convectively heated incubators,” Computers in biology and medicine, vol. 57, pp. 159–172, 2015.
There are 34 citations in total.

Details

Primary Language English
Subjects Software Engineering (Other)
Journal Section Research Article
Authors

Hatice Kabaoğlu 0000-0002-1077-0012

Fecir Duran 0000-0001-7256-5471

Emine Uçar 0000-0002-6838-3015

Early Pub Date June 23, 2025
Publication Date June 30, 2025
Submission Date February 3, 2025
Acceptance Date May 29, 2025
Published in Issue Year 2025Volume: 8 Issue: 2

Cite

APA Kabaoğlu, H., Duran, F., & Uçar, E. (2025). The Development of Digital Twin Baby Incubators for Fault Detection and Performance Analysis. Sakarya University Journal of Computer and Information Sciences, 8(2), 301-311. https://doi.org/10.35377/saucis...1612668
AMA Kabaoğlu H, Duran F, Uçar E. The Development of Digital Twin Baby Incubators for Fault Detection and Performance Analysis. SAUCIS. June 2025;8(2):301-311. doi:10.35377/saucis.1612668
Chicago Kabaoğlu, Hatice, Fecir Duran, and Emine Uçar. “The Development of Digital Twin Baby Incubators for Fault Detection and Performance Analysis”. Sakarya University Journal of Computer and Information Sciences 8, no. 2 (June 2025): 301-11. https://doi.org/10.35377/saucis. 1612668.
EndNote Kabaoğlu H, Duran F, Uçar E (June 1, 2025) The Development of Digital Twin Baby Incubators for Fault Detection and Performance Analysis. Sakarya University Journal of Computer and Information Sciences 8 2 301–311.
IEEE H. Kabaoğlu, F. Duran, and E. Uçar, “The Development of Digital Twin Baby Incubators for Fault Detection and Performance Analysis”, SAUCIS, vol. 8, no. 2, pp. 301–311, 2025, doi: 10.35377/saucis...1612668.
ISNAD Kabaoğlu, Hatice et al. “The Development of Digital Twin Baby Incubators for Fault Detection and Performance Analysis”. Sakarya University Journal of Computer and Information Sciences 8/2 (June 2025), 301-311. https://doi.org/10.35377/saucis. 1612668.
JAMA Kabaoğlu H, Duran F, Uçar E. The Development of Digital Twin Baby Incubators for Fault Detection and Performance Analysis. SAUCIS. 2025;8:301–311.
MLA Kabaoğlu, Hatice et al. “The Development of Digital Twin Baby Incubators for Fault Detection and Performance Analysis”. Sakarya University Journal of Computer and Information Sciences, vol. 8, no. 2, 2025, pp. 301-1, doi:10.35377/saucis. 1612668.
Vancouver Kabaoğlu H, Duran F, Uçar E. The Development of Digital Twin Baby Incubators for Fault Detection and Performance Analysis. SAUCIS. 2025;8(2):301-1.
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