Predicting genetic disorder and types of disorder using chain classifier approach

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dc.contributor.authorRaza, Ali
dc.contributor.authorRustam, Furqan
dc.contributor.authorSiddiqui, Hafeez Ur Rehman
dc.contributor.authorTorre Díez, Isabel de la
dc.contributor.authorGarcía-Zapirain, Begoña
dc.contributor.authorLee, Ernesto
dc.contributor.authorAshraf, Imran
dc.date.accessioned2026-02-26T11:24:16Z
dc.date.available2026-02-26T11:24:16Z
dc.date.issued2023
dc.date.updated2026-02-26T11:24:16Z
dc.description.abstractGenetic disorders are the result of mutation in the deoxyribonucleic acid (DNA) sequence which can be developed or inherited from parents. Such mutations may lead to fatal diseases such as Alzheimer’s, cancer, Hemochromatosis, etc. Recently, the use of artificial intelligence-based methods has shown superb success in the prediction and prognosis of different diseases. The potential of such methods can be utilized to predict genetic disorders at an early stage using the genome data for timely treatment. This study focuses on the multi-label multi-class problem and makes two major contributions to genetic disorder prediction. A novel feature engineering approach is proposed where the class probabilities from an extra tree (ET) and random forest (RF) are joined to make a feature set for model training. Secondly, the study utilizes the classifier chain approach where multiple classifiers are joined in a chain and the predictions from all the preceding classifiers are used by the conceding classifiers to make the final prediction. Because of the multi-label multi-class data, macro accuracy, Hamming loss, and α-evaluation score are used to evaluate the performance. Results suggest that extreme gradient boosting (XGB) produces the best scores with a 92% α-evaluation score and a 84% macro accuracy score. The performance of XGB is much better than state-of-the-art approaches, in terms of both performance and computational complexity.en
dc.description.sponsorshipThis research was supported by the European University of the Atlanticen
dc.identifier.citationRaza, A., Rustam, F., Siddiqui, H. U. R., Diez, I. d. l. T., Garcia-Zapirain, B., Lee, E., & Ashraf, I. (2023). Predicting genetic disorder and types of disorder using chain classifier approach. Genes, 14(1). https://doi.org/10.3390/GENES14010071
dc.identifier.doi10.3390/GENES14010071
dc.identifier.eissn2073-4425
dc.identifier.urihttps://hdl.handle.net/20.500.14454/5251
dc.language.isoeng
dc.publisherMDPI
dc.rights© 2022 by the authors
dc.subject.otherChain classifier approach
dc.subject.otherGenetic disorder
dc.subject.otherGenome mutation
dc.subject.otherMachine learning
dc.titlePredicting genetic disorder and types of disorder using chain classifier approachen
dc.typejournal article
dcterms.accessRightsopen access
oaire.citation.issue1
oaire.citation.titleGenes
oaire.citation.volume14
oaire.licenseConditionhttps://creativecommons.org/licenses/by/4.0/
oaire.versionVoR
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