Early Detection and Recommendation of Anemia in Adolescent Females  Using Artificial Intelligence

Adinda Navis Nurlailiah; M. Choiroel Anwar; Kurnianingsih

doi:10.61096/ijamscr.v11.iss3.2023.325-328

Issue

Vol. 11 No. 3 (2023): 2023 Volume -11 - Issue 3

Issue Published : July 1, 2023

Date Log

Submitted

September 16, 2023

Published

September 16, 2023

This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Early Detection and Recommendation of Anemia in Adolescent Females Using Artificial Intelligence

https://doi.org/10.61096/ijamscr.v11.iss3.2023.325-328

Adinda Navis Nurlailiah

Department of Midwifery Master of Applied Science in Midwifery Program, Semarang Health Polytecnic, Semarang, Indonesia

M. Choiroel Anwar

Semarang Health Polytecnic, Semarang, Indonesia.

Kurnianingsih

Semarang State Polytecnic, Semarang, Indonesia.

Corresponding Author(s) : Adinda Navis Nurlailiah

navisadinda@gmail.com

International Journal of Allied Medical Sciences and Clinical Research, Vol. 11 No. 3 (2023): 2023 Volume -11 - Issue 3
Article Published : September 16, 2023

Abstract

Background: The usual detection of anemia is checking by hemoglobin (Hb) levels. But the process requires quite a lot of preparation and implementation stages so the adolescent can not do it independently, in real-time, and anywhere. Therefore, an early detection system for anemia in adolescent females was developed using artificial intelligence. Where in the system will display the results of anemia detection based on signs, symptoms and risk factors as well as recommendations for anemia prevention.

Purposes: Innovate early detection systems and recommendations for anemia in adolescent females using artificial intelligence based on system accuracy values.

Methods: The method in this research is the research and development method. Using 430 data on signs of symptoms, risk factors, and Hb examination results for adolescent females to build a detection and recommendations system.

Results: Based on 4 algorithm tests, the highest system accuracy value is 81.3% using K-nearest neighbors (KNN) and the recommendation accuracy is 90.9%.

Conclusion: The study is proven to be able to build an anemia detection system using artificial intelligence K-Nearest Neighbor with the result of a system accuracy rate of 81,3%, and the recommendation system for anemia uses artificial intelligence forward chaining method with the results of a system accuracy rate of 90,9%.

Keywords

early detection anemia adolescent females artificial intelligence

Adinda Navis Nurlailiah, M. Choiroel Anwar, & Kurnianingsih. (2023). Early Detection and Recommendation of Anemia in Adolescent Females Using Artificial Intelligence. International Journal of Allied Medical Sciences and Clinical Research, 11(3), 325–328. https://doi.org/10.61096/ijamscr.v11.iss3.2023.325-328

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References

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Lopez A, Cacoub P, Macdougall IC, Peyrin B, Laurent MI. Iron deficiency anemia. Lancet. 2016;387(10021):907-16. doi: 10.1016/S0140-6736(15)60865-0, PMID 26314490.
Sari VM, Rahmatika SD. Gambaran Kejadian anemia pada Remaja Putri di Kabupaten Cirebon. Colostrum J Kebidanan. 2021;2(2):33-7. eISSN: 2716-0114.
Megawati M, Subianto T. Nurvita N. Penjaringan dan Penatalaksanaan Kejadian anemia pada Remaja Putri Usia 12-18 Tahun di MA Athoriyah Kecamatan Cikatomas Kabupaten Tasikmalaya. Prosiding Seminar Nasional & Diseminasi Hasil Pengabdian kepada Masyarakat Berbasis Riset (April). Vols. 158-62. e-ISBN:978-602-51817-0-2; 2018.
Karagül Yıldız T, Yurtay N, Öneç B. Classifying anemia types using artificial learning methods. Eng Sci Technol An Int J. 2021;24(1):50-70. doi: 10.1016/j.jestch.2020.12.003.
Sulardi N, Witanti A. Sistem pakar untuk diagnosis penyakit anemia menggunakan teorema Bayes. J Tekn Inform. 2020;1(1):19-24. doi: 10.20884/1.jutif.2020.1.1.12.
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Ahmed HE. AI advantages and disadvantages. Int J Sci Eng Appl Sci (IJSEAS). 2018;4(4):22-5. ISSN: 2395-3470.
Jaiswal M, Srivastava A, Siddiqui TJ. Machine learning algorithms for anemia disease prediction. Singapore: Springer Singapore. p. 2019.463-469. doi: 10.1007/978-981-13-2685-1.
Ekasanti I, Adi AC, Yono M, Nirmala G F, Isfandiari MA. Determinants of anemia among early adolescent girls in Kendari city. Amerta Nutr. 2020;4(4):271. doi: 10.20473/amnt.v4i4.2020.271-279.
Anissa K. Pengembangan instrumen tes untuk mendeteksi gejala diabetes Melitus dalam kehamilan. p. 40-5; 2020. Poltekkes Kemenkes Semarang. Available from: https://repository.poltekkes-smg.ac.id.
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Dewi AMSI, Dwidasmara IBG. Implementation of the K-nearest neighbor (KNN) algorithm for classification of obesity levels. JELIKU;9(2). doi: 10.24843/JLK.2020.v09.i02.p15.
Oktaviana F, Widyawati MN, Kurnianingsih. Deteksi Dini dan rekomendasi stunting pada ibu hamil berbasis android dengan teknik forward chaining; 2020. Poltekkes Kemenkes Semarang. Available from: http://repository.poltekkes-smg.ac.id.
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World Health Organization (WHO). Global nutrition target. Department of Nutrition for Health and Development. WHO/NMH/NH. Vol. 2025 Anaemia Police Brief; 2014. Available from: https://www.who.int/publications/i/item/WHO-NMH-NHD-14.4.

References

Kementerian Kesehatan RI. Badan Penelitian dan Pengembangan. Hasil utama riset kesehatan dasar 2018. Kementerian Kesehatan. Republik Indonesia. 2019;1-100.

Lopez A, Cacoub P, Macdougall IC, Peyrin B, Laurent MI. Iron deficiency anemia. Lancet. 2016;387(10021):907-16. doi: 10.1016/S0140-6736(15)60865-0, PMID 26314490.

Sari VM, Rahmatika SD. Gambaran Kejadian anemia pada Remaja Putri di Kabupaten Cirebon. Colostrum J Kebidanan. 2021;2(2):33-7. eISSN: 2716-0114.

Megawati M, Subianto T. Nurvita N. Penjaringan dan Penatalaksanaan Kejadian anemia pada Remaja Putri Usia 12-18 Tahun di MA Athoriyah Kecamatan Cikatomas Kabupaten Tasikmalaya. Prosiding Seminar Nasional & Diseminasi Hasil Pengabdian kepada Masyarakat Berbasis Riset (April). Vols. 158-62. e-ISBN:978-602-51817-0-2; 2018.

Karagül Yıldız T, Yurtay N, Öneç B. Classifying anemia types using artificial learning methods. Eng Sci Technol An Int J. 2021;24(1):50-70. doi: 10.1016/j.jestch.2020.12.003.

Sulardi N, Witanti A. Sistem pakar untuk diagnosis penyakit anemia menggunakan teorema Bayes. J Tekn Inform. 2020;1(1):19-24. doi: 10.20884/1.jutif.2020.1.1.12.

Mannino RG, Myers DR, Tyburski EA, Caruso C, Boudreaux J, Leong T, et al. Smartphone app for non-invasive detection of anemia using only patient-sourced photos. Nat Commun. 2018;9(1):4924. doi: 10.1038/s41467-018-07262-2, PMID 30514831.

Hanum Z. Kemenkominfo: 89% penduduk Indonesia gunakan smartphone. Media Indonesia; 2021 Maret.

Adiansah W, Setiawan E, Kodaruddin WN, Wibowo H. Person in environment remaja pada Era revolusi industri 4.0. Focus. 2019;2(1):47. doi: 10.24198/focus.v2i1.23118.

Ningsih EW, Fajrin HR, Fitriyah A. Pendeteksi hemoglobin non invasive. Medika Teknika J Tekn Elektromedik Indones. 2019;1(1). doi: 10.18196/mt.010102.

Ahmed HE. AI advantages and disadvantages. Int J Sci Eng Appl Sci (IJSEAS). 2018;4(4):22-5. ISSN: 2395-3470.

Jaiswal M, Srivastava A, Siddiqui TJ. Machine learning algorithms for anemia disease prediction. Singapore: Springer Singapore. p. 2019.463-469. doi: 10.1007/978-981-13-2685-1.

Ekasanti I, Adi AC, Yono M, Nirmala G F, Isfandiari MA. Determinants of anemia among early adolescent girls in Kendari city. Amerta Nutr. 2020;4(4):271. doi: 10.20473/amnt.v4i4.2020.271-279.

Anissa K. Pengembangan instrumen tes untuk mendeteksi gejala diabetes Melitus dalam kehamilan. p. 40-5; 2020. Poltekkes Kemenkes Semarang. Available from: https://repository.poltekkes-smg.ac.id.

Kementerian kesehatan republik Indonesia. [Pedoman Pencegahan dan Penanggulangan anemia Pada Remaja Putri dan Wanita Usia Subur]. Kementerian kesehatan republik Indonesia. 2018.

Wibowo N, Irwinda R, Hiksas R. Anemia Defisiensi Besi pada Kehamilan. 1st ed. Depok: UI Publishing. ISBN: 978-623-333-041-1; 2021.

Kementerian Kesehatan RI. Jakarta: kementerian kesehatan republik Indonesia. Profil kesehatan Indonesia tahun. 1st ed Hardhana B, Sibuea F, Widiantini W, editors. Vol. 2020. ISBN: 978-602-416-977-0; 2019.

Kementrian kesehatan republik Indonesia. Pedoman Pencegahan dan Penanggulangan Anemia pada Remaja Putri dan W. Usia Subur. 1st ed. Jakarta: Kementerian Kesehatan Republik Indonesia, 2018.

Primayanti I, Geriputri NN. A MY, Danianto A, Rizkinov MJ, S RH. Skrining anemia pada siswi SMA negeri 1 Praya. In: Mataram PPEPADU, editor. Available from: https://jurnal.lppm.unram.ac.id/index.php/prosidingpepadu/article/view/26. LPPM universitas Mataram. Vols. 165-9; 2019.

Proverawati A. Anemia dan anemia Kehamilan. Yogyakarta: Nuha Medika. ISBN: 978-602-912-921-2; 2011.

Fuadah YN, Sa’idah S, Wijayanto I, Patmasari R, Magdalena R. Non invasive anemia detection in pregnant women based on digital image processing and K-nearest neighbor. In: 3rd International Conference on Biomedical Engineering (IBIOMED). IEEE Publications; 2020. p. 60-4. doi: 10.1109/IBIOMED50285.2020.9487605.

Dewi AMSI, Dwidasmara IBG. Implementation of the K-nearest neighbor (KNN) algorithm for classification of obesity levels. JELIKU;9(2). doi: 10.24843/JLK.2020.v09.i02.p15.

Oktaviana F, Widyawati MN, Kurnianingsih. Deteksi Dini dan rekomendasi stunting pada ibu hamil berbasis android dengan teknik forward chaining; 2020. Poltekkes Kemenkes Semarang. Available from: http://repository.poltekkes-smg.ac.id.

Al-ajlan A. The comparison between forward and backward chaining. Int J Mach Learn Comput. 2015;5(2):106-13. doi: 10.7763/IJMLC.2015.V5.492.

World Health Organization (WHO). Global nutrition target. Department of Nutrition for Health and Development. WHO/NMH/NH. Vol. 2025 Anaemia Police Brief; 2014. Available from: https://www.who.int/publications/i/item/WHO-NMH-NHD-14.4.

Author biographies is not available.