Expert System for Diagnosis Pregnancy Disorders using Forward Chaining Method Based on Android

Dina Safitri(1), Safuan Safuan(2*), Luqman Assaffat(3)


(1) Universitas Muhammadiyah Semarang
(2) Universitas Muhammadiyah Semarang
(3) Universitas Muhammadiyah Semarang
(*) Corresponding Author

Abstract


Technology's rapid evolution has extended its impact into the healthcare field, including the development of artificial intelligence-based expert systems designed to streamline the work processes of nurses and obstetricians. In this research, we use the forward chaining method to build an android-based expert system for diagnosing fetal disorders in pregnant women. This system is made for ease of use on mobile devices by targeting pregnant women where this application provides a self-detection mechanism for pregnancy abnormalities. The test results show a high level of respondent satisfaction with this expert system application, with an average score of 90.16%, indicating a strong acceptance of the quality and functionality of the application. It can be concluded that our proposed expert system application shows a positive response from respondents and is considered successful in providing pregnancy diagnosis services independently.

Keywords


forward chaining; expert system; fetal health; pregnancy

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References


Dairoh, Sasmito, G.W., Raharjo, G., 2023. Implementation of an expert system in diagnosing children’s mental disorders using the forward chaining method, in: 3RD BOROBUDUR INTERNATIONAL SYMPOSIUM ON SCIENCE AND TECHNOLOGY 2021. p. 020012. https://doi.org/10.1063/5.0120398

Dewi, A., Sugiyo, D., Sundari, S., Puspitosari, W.A., Supriyatiningsih, Dewi, T.S., 2023. Research implementation and evaluation of the maternity waiting home program for enhancing maternal health in remote area of Indonesia. Clin. Epidemiol. Glob. Heal. 23, 101369. https://doi.org/10.1016/j.cegh.2023.101369

Garapati, J., Jajoo, S., Aradhya, D., Reddy, L.S., Dahiphale, S.M., Patel, D.J., 2023. Postpartum Mood Disorders: Insights into Diagnosis, Prevention, and Treatment. Cureus. https://doi.org/10.7759/cureus.42107

Ilham, A., Kindarto, A., Kareem Oleiwi, A., Khikmah, L., 2024. CFCM-SMOTE: A Robust Fetal Health Classification to Improve Precision Modelling in Multi-Class Scenarios. Int. J. Comput. Digit. Syst. 15, 1–9.

Lintern, G., Motavalli, A., 2018. Healthcare information systems: the cognitive challenge. BMC Med. Inform. Decis. Mak. 18, 3. https://doi.org/10.1186/s12911-018-0584-z

Mennickent, D., Rodríguez, A., Opazo, M.C., Riedel, C.A., Castro, E., Eriz-Salinas, A., Appel-Rubio, J., Aguayo, C., Damiano, A.E., Guzmán-Gutiérrez, E., Araya, J., 2023. Machine learning applied in maternal and fetal health: a narrative review focused on pregnancy diseases and complications. Front. Endocrinol. (Lausanne). 14. https://doi.org/10.3389/fendo.2023.1130139

Skudder-Hill, L., 2020. Maternal Mortality and Sustainable Development, in: Good Health and Well-Being. Springer, pp. 461–471. https://doi.org/10.1007/978-3-319-95681-7_42

Yuliana, Y., Noviyanti, N., 2021. An Expert System for Diagnosing Mental Disorders Using the Web-Based Forward Chaining Method. J. Tek. Inf. dan Komput. 4, 220. https://doi.org/10.37600/tekinkom.v4i2.373


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DOI: https://doi.org/10.26714/jichi.v4i2.13013

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____________________________________________________________________________
Journal of Intelligent Computing and Health Informatics (JICHI)
ISSN 2715-6923 (print) | 2721-9186 (online)
Organized by
Department of Informatics
Faculty of Engineering
Universitas Muhammadiyah Semarang

W : https://jurnal.unimus.ac.id/index.php/ICHI
E : [email protected], [email protected]

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