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Prediction of side effects of drug resistant tuberculosis drugs using multi-label random forest

IAES International Journal of Artificial Intelligence · Vol. 14 No. 4 (2025) · pp. 2899–2908

DOI
10.11591/ijai.v14.i4.pp2899-2908
ISSN
2252-8938, 2089-4872
Published
2025-08-01
Publisher
Institute of Advanced Engineering and Science
Language
eng
License
creativecommons.org/licenses/by-sa/4.0

Abstract

Drug-resistant tuberculosis (DR-TB) has become a concern because anti-tuberculosis drugs (ATD) used to treat it can cause side effects in patients. This study aimed to predict the potential side effects of ATD using a multi-label classification approach with a random forest (RF) algorithm. This study used 660 medical record data, including the 14 ATD treatments prescribed to the patients and the six side effects experienced by patients. The model was trained using the best parameters based on the hyperparameter tuning process. The results show that the RF multi-label algorithm can be an alternative for building ATD side effect prediction models because it produces the most optimal performance value compared to the decision tree (DT) and extreme gradient boosting (XGBoost). The area under the curve (AUC) score of all RF multi-label models is above 0.8, which means that all RF multi-label models are considered acceptable and applicable for ATD side effect prediction. In addition, eight features influenced the models based on the average feature importance score of the RF models. This study is expected to help predict the side effects of ATD used to treat DR-TB based on ATD treatment and determine the most promising tree-based machine learning algorithm for predicting ATD side effects.

Keywords

Collaboration network of the authors

40 authors · 138 co-authorship links · 2 across different organisations · showing the 40 most connected

Hover or tap a name to highlight its co-authors. Double-click to open the author's page.

  • IPB University
  • Universitas Kristen Wira Wacana Sumba
  • Affiliation unknown
  • Different organisations
  • Same organisation
  • Affiliation unknown
  • Thicker line = more joint articles · bigger circle = more articles
Show as table
Co-authorOrganisationJoint articles
Annisa AnnisaIPB University3
Muh Fadhil Al-Haaq GinogaIPB University2
Agus BuonoIPB University1
Arini Aha PekuwaliUniversitas Kristen Wira Wacana Sumba1
Albert AdrianusIPB University1
Larasati LarasatiIPB University1
Nabila Sekar RamadhantiIPB University1
Hendra RahmawanIPB University1
Refianto Damai DarmawanIPB University1
Widyastuti WidyastutiAffiliation unknown1
Anna RozaliyaniAffiliation unknown1
Ary Indriana SavitriAffiliation unknown1
Findra SetianingrumAffiliation unknown1
Titania Nur ShellyAffiliation unknown1
Vini RatnasariAffiliation unknown1
Romala KuswindartiAffiliation unknown1
Ngabila SalamaAffiliation unknown1
Dwi OktaviaAffiliation unknown1
Luh Karunia WahyuniAffiliation unknown1
Dita AditianingsihAffiliation unknown1
Peggy SunarjoAffiliation unknown1
Retno Asti WerdhaniAffiliation unknown1
Kristiana Siste KurniasantiAffiliation unknown1
Anitta Florence Stans PaulusAffiliation unknown1
Mellisya RamadhanyAffiliation unknown1
Widjajalaksmi KusumaningsihAffiliation unknown1
Imas Sukaesih SitanggangIPB University1
Aditya Kusuma SetyanegaraAffiliation unknown1
Mohammad Romano DiansyahAffiliation unknown1
Mohamad RafiAffiliation unknown1
Dewi Anggraini SeptaningsihAffiliation unknown1
Auliya IlmiawatiAffiliation unknown1
Laela WulansariAffiliation unknown1
Tsania Firqin PurnomoAffiliation unknown1
Reza AuliatifaniAffiliation unknown1
Khaydanur KhaydanurAffiliation unknown1

Affiliations come from OpenAlex; journal OAI-PMH metadata carries none. Authors without an OpenAlex ID are matched by name, which can merge or split people.

Metadata source: OAI-PMH (metadata jurnal) http://ijai.iaescore.com/index.php/IJAI/oai, first harvested 8 Nov 2026, last seen 8 Nov 2026.
Original metadata (oai_dc) 
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	<dc:title xml:lang="en-US">Prediction of side effects of drug resistant tuberculosis drugs using multi-label random forest</dc:title>
	<dc:creator>Helma, Siti Syahidatul</dc:creator>
	<dc:creator>Kusuma, Wisnu Ananta</dc:creator>
	<dc:creator>Mushthofa, Mushthofa</dc:creator>
	<dc:creator>Handayani, Diah</dc:creator>
	<dc:subject xml:lang="en-US">Random Forest; Tree Based Algorithm; Multi-label Learning;</dc:subject>
	<dc:subject xml:lang="en-US">Drug side effects; Drug-resistant tuberculosis; Multi-label classification; Tree-based learning; Tuberculosis</dc:subject>
	<dc:description xml:lang="en-US">Drug-resistant tuberculosis (DR-TB) has become a concern because anti-tuberculosis drugs (ATD) used to treat it can cause side effects in patients. This study aimed to predict the potential side effects of ATD using a multi-label classification approach with a random forest (RF) algorithm. This study used 660 medical record data, including the 14 ATD treatments prescribed to the patients and the six side effects experienced by patients. The model was trained using the best parameters based on the hyperparameter tuning process. The results show that the RF multi-label algorithm can be an alternative for building ATD side effect prediction models because it produces the most optimal performance value compared to the decision tree (DT) and extreme gradient boosting (XGBoost). The area under the curve (AUC) score of all RF multi-label models is above 0.8, which means that all RF multi-label models are considered acceptable and applicable for ATD side effect prediction. In addition, eight features influenced the models based on the average feature importance score of the RF models. This study is expected to help predict the side effects of ATD used to treat DR-TB based on ATD treatment and determine the most promising tree-based machine learning algorithm for predicting ATD side effects.</dc:description>
	<dc:publisher xml:lang="en-US">Institute of Advanced Engineering and Science</dc:publisher>
	<dc:contributor xml:lang="en-US">the Directorate of Higher Education, Research and Technology, Ministry of Education, Culture, Research and Technology Republic of Indonesia</dc:contributor>
	<dc:date>2025-08-01</dc:date>
	<dc:type>info:eu-repo/semantics/article</dc:type>
	<dc:type>info:eu-repo/semantics/publishedVersion</dc:type>
	<dc:type xml:lang="en-US"></dc:type>
	<dc:format>application/pdf</dc:format>
	<dc:identifier>https://ijai.iaescore.com/index.php/IJAI/article/view/24939</dc:identifier>
	<dc:identifier>10.11591/ijai.v14.i4.pp2899-2908</dc:identifier>
	<dc:source xml:lang="en-US">IAES International Journal of Artificial Intelligence (IJ-AI); Vol 14, No 4: August 2025; 2899-2908</dc:source>
	<dc:source>2252-8938</dc:source>
	<dc:source>2089-4872</dc:source>
	<dc:source>10.11591/ijai.v14.i4</dc:source>
	<dc:language>eng</dc:language>
	<dc:relation>https://ijai.iaescore.com/index.php/IJAI/article/view/24939/14629</dc:relation>
	<dc:rights xml:lang="en-US">Copyright (c) 2025 Institute of Advanced Engineering and Science</dc:rights>
	<dc:rights xml:lang="en-US">http://creativecommons.org/licenses/by-sa/4.0</dc:rights>
</oai_dc:dc>