RT info:eu-repo/semantics/article
T1 Predicting genetic disorder and types of disorder using chain classifier approach
A1 Raza, Ali
A1 Rustam, Furqan
A1 Siddiqui, Hafeez Ur Rehman
A1 Torre Díez, Isabel de la
A1 Garcia Zapirain, Begonya
A1 Lee, Ernesto
A1 Ashraf, Imran
K1 Genetics
K1 Human genetics
K1 Genética humana
K1 Mutation (Biologie)
K1 Mutación (Biología)
K1 Genetic disorders
K1 Machine learning
K1 Aprendizaje automático
K1 Chain classifier approach
K1 Enfoque clasificador de cadena
K1 2409 Genética
K1 2409.02 Ingeniería Genética
AB Genetic 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.
PB MDPI
SN 2073-4425
YR 2023
FD 2023
LK https://uvadoc.uva.es/handle/10324/60685
UL https://uvadoc.uva.es/handle/10324/60685
LA eng
NO Genes, 2023, Vol. 14, Nº. 1, 71
NO Producción Científica
DS UVaDOC
RD 14-jul-2024