nach oben

Erschienen in:

2024 | OriginalPaper | Buchkapitel

Risk Prediction of Maternal Health by Model Analysis Using Artificial Intelligence

verfasst von : Anandakumar Haldorai, Babitha Lincy R, Suriya Murugan, Minu Balakrishnan

Erschienen in: Artificial Intelligence for Sustainable Development

Verlag: Springer Nature Switzerland

Einloggen

Aktivieren Sie unsere intelligente Suche, um passende Fachinhalte oder Patente zu finden.

search-config

KI-gestützte Suche

Aus

Abstract

Medical practice is being gradually transformed by artificial intelligence (AI). Diabetes mellitus (DM) is a disease characterized by inadequate control of blood glucose levels, and has the potential to create a healthcare crisis worldwide. Pregnant women who have gestational diabetes mellitus (GDM) may put their unborn children at risk. This form of diabetes can result in larger-than-normal offspring, making vaginal birth more difficult. This chapter presents a case study that uses an analysis of different machine learning methods to suggest a machine learning model for the early detection of gestational diabetes mellitus and the probability that it would proceed to type 2 diabetes.

Sie haben noch keine Lizenz? Dann Informieren Sie sich jetzt über unsere Produkte:

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

über 102.000 Bücher
über 537 Zeitschriften

aus folgenden Fachgebieten:

Automobil + Motoren
Bauwesen + Immobilien
Business IT + Informatik
Elektrotechnik + Elektronik
Energie + Nachhaltigkeit
Finance + Banking
Management + Führung
Marketing + Vertrieb
Maschinenbau + Werkstoffe
Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Springer Professional "Technik"

Online-Abonnement

Mit Springer Professional "Technik" erhalten Sie Zugriff auf:

über 67.000 Bücher
über 390 Zeitschriften

aus folgenden Fachgebieten:

Automobil + Motoren
Bauwesen + Immobilien
Business IT + Informatik
Elektrotechnik + Elektronik
Energie + Nachhaltigkeit
Maschinenbau + Werkstoffe

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Springer Professional "Wirtschaft"

Online-Abonnement

Mit Springer Professional "Wirtschaft" erhalten Sie Zugriff auf:

über 67.000 Bücher
über 340 Zeitschriften

aus folgenden Fachgebieten:

Bauwesen + Immobilien
Business IT + Informatik
Finance + Banking
Management + Führung
Marketing + Vertrieb
Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Vorheriges Kapitel Detecting Alzheimer’s Disease Using Deep Learning Framework for Medial IoT Application

Nächstes Kapitel Hemorrhage Detection from Whole-Body CT Images Using Deep Learning

C. Bayliss, “Machine learning based simulation optimisation for urban routing problems,” Applied Soft Computing, vol. 105, p. 107269, Jul. 2021, https://doi.org/10.1016/j.asoc.2021.107269.

Kabir Ali, “A Review of Emotions, Behavior and Cognition”, Journal of Biomedical and Sustainable Healthcare Applications, vol.3, no.2, pp. 165–176, July 2023. https://doi.org/10.53759/0088/JBSHA202303016.

E. Jyothi Kiranmayi, N.V. Rao and K.S. Nayanathara, “Hybrid Interval Type-2 Fuzzy AHP and COPRAS-G-based trusted neighbour node Discovery in Wireless Sensor Networks, Journal of Machine and Computing, vol.3, no.3, pp. 251–263, July 2023. https://doi.org/10.53759/7669/jmc202303023.

H. Li and J. Sansalone, “Implementing machine learning to optimize the cost-benefit of urban water clarifier geometrics,” Water Research, vol. 220, p. 118685, Jul. 2022, https://doi.org/10.1016/j.watres.2022.118685.

H. Tanim, C. B. McRae, H. Tavakol-Davani, and E. Goharian, “Flood Detection in Urban Areas Using Satellite Imagery and Machine Learning,” Water, vol. 14, no. 7, p. 1140, Apr. 2022, https://doi.org/10.3390/w14071140.

L. Wei, W. Tian, E. A. Silva, R. Choudhary, Q. Meng, and S. Yang, “Comparative Study on Machine Learning for Urban Building Energy Analysis,” Procedia Engineering, vol. 121, pp. 285–292, 2015, https://doi.org/10.1016/j.proeng.2015.08.1070.CrossRef

Anna Alsufieva and Daria Aleeva, “A Review of the Definition and Significance of the Brain Health”, Journal of Biomedical and Sustainable Healthcare Applications, vol.3, no.2, pp. 129–140, July 2023. https://doi.org/10.53759/0088/JBSHA202303013.

T. Thamaraimanalan et al., “Machine Learning based Patient Mental Health Prediction using Spectral Clustering and RBFN Algorithms,” 2022 8th International Conference on Advanced Computing and Communication Systems (ICACCS), Mar. 2022, https://doi.org/10.1109/icaccs54159.2022.9785142.

S. Satheesh Kumar and Manjula Sanjay Koti, “Deep Learning Based Hybrid Network Architecture to Diagnose IoT Sensor Signal in Healthcare System”, Journal of Machine and Computing, pp. 103–114, April 2023. https://doi.org/10.53759/7669/jmc202303011.

10.

John Huria Nderitu, “AMental State Adaptive Interfaces as a Remedy to the Issue of Long-term, Continuous Human Machine Interaction”, Journal of Robotics Spectrum, vol.1, pp. 078–089, 2023. https://doi.org/10.53759/9852/JRS202301008.

11.

S. Ayub, R. Boddu, H. Verma, S. Revathi B, B. K. Saraswat, and A. Haldorai, “Health Index Estimation of Wind Power Plant Using Neurofuzzy Modeling,” Computational and Mathematical Methods in Medicine, vol. 2022, pp. 1–8, May 2022, https://doi.org/10.1155/2022/9535254.

12.

K. Nandini and G. Fathima, “Urban Air Quality Analysis and Prediction Using Machine Learning,” 2019 1st International Conference on Advanced Technologies in Intelligent Control, Environment, Computing and Communication Engineering (ICATIECE), Mar. 2019, https://doi.org/10.1109/icatiece45860.2019.9063845.

13.

Z. Lv, J. Li, C. Dong, H. Li, and Z. Xu, “Deep learning in the COVID-19 epidemic: A deep model for urban traffic revitalization index,” Data and Knowledge Engineering, vol. 135, p. 101912, Sep. 2021, https://doi.org/10.1016/j.datak.2021.101912.

14.

M. S. Hossain and G. Muhammad, “Environment Classification for Urban Big Data Using Deep Learning,” in IEEE Communications Magazine, vol. 56, no. 11, pp. 44–50, November 2018, https://doi.org/10.1109/MCOM.2018.1700577.

15.

16.

C. Karyotis, T. Maniak, F. Doctor, R. Iqbal, V. Palade and R. Tang, “Deep Learning for Flood Forecasting and Monitoring in Urban Environments,” 2019 18th IEEE International Conference On Machine Learning And Applications (ICMLA), Boca Raton, FL, USA, 2019, pp. 1392–1397, https://doi.org/10.1109/ICMLA.2019.00227.

17.

G. Garcia-Zanabria et al., “Mirante: A visualization tool for analyzing urban crimes,” 2020 33rd SIBGRAPI Conference on Graphics, Patterns and Images (SIBGRAPI), Porto de Galinhas, Brazil, 2020, pp. 148–155, https://doi.org/10.1109/SIBGRAPI51738.2020.00028.

18.

J. Samuel et al., “UD-SV: Urban data services and visualization framework for sharing multidisciplinary research,” Transactions in GIS, vol. 27, no. 3, pp. 841–858, Apr. 2023, https://doi.org/10.1111/tgis.13049.

19.

Pérez-Messina, E. Graells-Garrido, M. J. Lobo, and C. Hurter, “Modalflow: Cross-Origin Flow Data Visualization for Urban Mobility,” Algorithms, vol. 13, no. 11, p. 298, Nov. 2020, https://doi.org/10.3390/a13110298.

Titel: Risk Prediction of Maternal Health by Model Analysis Using Artificial Intelligence
verfasst von: Anandakumar Haldorai
Babitha Lincy R
Suriya Murugan
Minu Balakrishnan
Verlag: Springer Nature Switzerland
Buch: Artificial Intelligence for Sustainable Development
Print ISBN: 978-3-031-53971-8

Electronic ISBN: 978-3-031-53972-5

Copyright-Jahr: 2024
DOI: https://doi.org/10.1007/978-3-031-53972-5_6

Neuer Inhalt

Bildnachweise

VDI-Icon, Profil Icon, inhalt2, Springer Professional Modul/© Springer Fachmedien Wiesbaden GmbH, Die Gewinner und Laudatoren des Sustainability Award in Automotive 2024/© Uli Regenscheit | ATZlive, Search Icon, Banner Hanser, Additiv gefertigte Teile/© Marina_Skoropadskaya | Getty Images | iStock, Warnschild "Land unter"/© Bluedesign / Fotolia, Gardiner von Trapp/© Alpega Group, Zeitschrift Wissensmanagement Cover, PatentFit-Logo/© Springer Fachmedien Wiesbaden GmbH, ATZ-Webinar: Prototypenfreie Entwicklung durch Offline- und Driver-in-the-Loop-HiL-Tests /© (c) VI-grade, chassis.tech plus 2023/© [M] ATZlive / TÜV SÜD PRODUCT SERVICE GMBH, adäsion-Webinar-Matinee/© krystiannawrocki_ Getty Images

Springer Professional

Abstract

Bitte loggen Sie sich ein, um Zugang zu Ihrer Lizenz zu erhalten.

Sie haben noch keine Lizenz? Dann Informieren Sie sich jetzt über unsere Produkte:

Springer Professional "Wirtschaft+Technik"

Springer Professional "Technik"

Springer Professional "Wirtschaft"

Neuer Inhalt

Bitte loggen Sie sich ein, um Zugang zu Ihrer Lizenz zu erhalten.

Bitte loggen Sie sich ein, um Zugang zu Ihrer Lizenz zu erhalten.