Journal Article

Evaluation of penalized and machine learning methods for asthma disease prediction in the Korean Genome and Epidemiology Study (KoGES)

Cha, Junho, Choi, Yongjun, Choi, Sungkyoung

2024

Overview

Background Genome-wide association studies have successfully identified genetic variants associated with human disease. Various statistical approaches based on penalized and machine learning methods have recently been proposed for disease prediction. In this study, we evaluated the performance of several such methods for predicting asthma using the Korean Chip (KORV1.1) from the Korean Genome and Epidemiology Study (KoGES). Results First, single-nucleotide polymorphisms were selected via single-variant tests using logistic regression with the adjustment of several epidemiological factors. Next, we evaluated the following methods for disease prediction: ridge, least absolute shrinkage and selection operator, elastic net, smoothly clipped absolute deviation, support vector machine, random forest, boosting, bagging, naïve Bayes, and k -nearest neighbor. Finally, we compared their predictive performance based on the area under the curve of the receiver operating characteristic curves, precision, recall, F1-score, Cohen′s Kappa, balanced accuracy, error rate, Matthews correlation coefficient, and area under the precision-recall curve. Additionally, three oversampling algorithms are used to deal with imbalance problems. Conclusions Our results show that penalized methods exhibit better predictive performance for asthma than that achieved via machine learning methods. On the other hand, in the oversampling study, randomforest and boosting methods overall showed better prediction performance than penalized methods.

• Publisher: BioMed Central,BioMed Central Ltd,Springer Nature B.V,BMC
• Subject: Accuracy / Algorithms / Asthma / Bioinformatics / Biomedical and Life Sciences / Body mass index / Computational Biology/Bioinformatics / Computer Appl. in Life Sciences / Correlation coefficient / Correlation coefficients / Datasets / Disease / Disease risk prediction model / Ensemble methods / Epidemiology / Genetic aspects / Genetic diversity / Genetic variance / Genome-wide association studies / Genomes / Large-scale genetic data / Learning algorithms / Life Sciences / Machine learning / Machine learning methods / Medical research / Medicine, Experimental / Microarrays / Nucleotides / Oversampling / Penalized methods / Performance evaluation / Performance prediction / Predictions / Recall / Risk factors / Single nucleotide polymorphisms / Single-nucleotide polymorphism / Statistical analysis / Support vector machines