Implementation of fuzzy logic approach for thalassemia screening in children

Redy Susanto, Erliyan; Syarif, Admi; Warsito, Warsito; Nisa Berawi, Khairun; Ayu Sangging, Putu Ristyaning; Wantoro, Agus

doi:10.11591/ijai.v13.i4.pp4062-4070

Akses terbuka Jurnal SINTA 1

Implementation of fuzzy logic approach for thalassemia screening in children

IAES International Journal of Artificial Intelligence · Vol. 13 No. 4 (2024) · hlm. 4062–4070

DOI: 10.11591/ijai.v13.i4.pp4062-4070
ISSN: 2252-8938, 2089-4872
Terbit: 2024-12-01
Penerbit: Institute of Advanced Engineering and Science
Bahasa: eng
Lisensi: creativecommons.org/licenses/by-sa/4.0

Abstrak

Thalassemia is one of the most dangerous blood disorders that can lead to severe complications. It is an inherited disease, usually detected after a child is two to four years old. Identification of thalassemia is a complex task, involving many variables. Doctors generally diagnose thalassemia by using a complete blood count (CBC) and high-performance liquid chromatography (HPLC) test results. However, HPLC tests are expensive and time consuming, hence the need for other methods to identify thalassemia. There are many studies on the application of artificial intelligence for medical applications. In this study, we developed a new fuzzy-based approach to identify thalassemia based on a patient’s blood laboratory results. First, we analyzed the CBC data for blood disorder prediction. Secondly, we adopt the test results of peripheral blood smear (PBS) to identify whether the person has thalassemia. We conducted several experiments using 30 (thirty) hospital patient data and the results were compared with the results provided by experts. The experimental results show that the system can determine blood disorders with 93% accuracy and 100% precision in thalassemia prediction. This system is very effective to help doctors in diagnosing thalassemia patients.

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Lampung University
Universitas Teknokrat Indonesia
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Rekan penulis	Organisasi	Artikel bersama
Rusliyawati Rusliyawati	Universitas Teknokrat Indonesia	2
Kurnia Muludi	Lampung University	1
Mohammad Surya Akbar	Lampung University	1
Dewi Asiah Shofiana	Lampung University	1
Sri Saptuti Wahyuningsih	Lampung University	1
Mahfut Mahfut	Lampung University	1
Putri Kendari	Lampung University	1
Endah Susiyanti	Afiliasi tidak diketahui	1

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Sumber metadata: OAI-PMH (metadata jurnal) http://ijai.iaescore.com/index.php/IJAI/oai, pertama di-harvest 8 Nov 2026, terakhir terlihat 8 Nov 2026.

Metadata asli (oai_dc)

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	<dc:title xml:lang="en-US">Implementation of fuzzy logic approach for thalassemia screening in children</dc:title>
	<dc:creator>Redy Susanto, Erliyan</dc:creator>
	<dc:creator>Syarif, Admi</dc:creator>
	<dc:creator>Warsito, Warsito</dc:creator>
	<dc:creator>Nisa Berawi, Khairun</dc:creator>
	<dc:creator>Ayu Sangging, Putu Ristyaning</dc:creator>
	<dc:creator>Wantoro, Agus</dc:creator>
	<dc:subject xml:lang="en-US">Artificial Intelligence; Fuzzy Logic;</dc:subject>
	<dc:subject xml:lang="en-US">Artificial intelligence; Blood disorder; Fuzzy approach; Inherited disease; Thalassemia</dc:subject>
	<dc:description xml:lang="en-US">Thalassemia is one of the most dangerous blood disorders that can lead to severe complications. It is an inherited disease, usually detected after a child is two to four years old. Identification of thalassemia is a complex task, involving many variables. Doctors generally diagnose thalassemia by using a complete blood count (CBC) and high-performance liquid chromatography (HPLC) test results. However, HPLC tests are expensive and time consuming, hence the need for other methods to identify thalassemia. There are many studies on the application of artificial intelligence for medical applications. In this study, we developed a new fuzzy-based approach to identify thalassemia based on a patient’s blood laboratory results. First, we analyzed the CBC data for blood disorder prediction. Secondly, we adopt the test results of peripheral blood smear (PBS) to identify whether the person has thalassemia. We conducted several experiments using 30 (thirty) hospital patient data and the results were compared with the results provided by experts. The experimental results show that the system can determine blood disorders with 93% accuracy and 100% precision in thalassemia prediction. This system is very effective to help doctors in diagnosing thalassemia patients.</dc:description>
	<dc:publisher xml:lang="en-US">Institute of Advanced Engineering and Science</dc:publisher>
	<dc:contributor xml:lang="en-US"></dc:contributor>
	<dc:date>2024-12-01</dc:date>
	<dc:type>info:eu-repo/semantics/article</dc:type>
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	<dc:format>application/pdf</dc:format>
	<dc:identifier>https://ijai.iaescore.com/index.php/IJAI/article/view/23576</dc:identifier>
	<dc:identifier>10.11591/ijai.v13.i4.pp4062-4070</dc:identifier>
	<dc:source xml:lang="en-US">IAES International Journal of Artificial Intelligence (IJ-AI); Vol 13, No 4: December 2024; 4062-4070</dc:source>
	<dc:source>2252-8938</dc:source>
	<dc:source>2089-4872</dc:source>
	<dc:source>10.11591/ijai.v13.i4</dc:source>
	<dc:language>eng</dc:language>
	<dc:relation>https://ijai.iaescore.com/index.php/IJAI/article/view/23576/14214</dc:relation>
	<dc:rights xml:lang="en-US">Copyright (c) 2024 Institute of Advanced Engineering and Science</dc:rights>
	<dc:rights xml:lang="en-US">http://creativecommons.org/licenses/by-sa/4.0</dc:rights>
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