Table 2 Prediction models for asthma development
From: Benchmarking omics-based prediction of asthma development in children
Method | Description | Refs. |
|---|---|---|
Linear models | ||
 LR | Logistic Regression models the probability of object belonging to a class by having the log-odds for the class to be a linear combination of features | [42] |
 LRCV | Logistic Regression with build-in validation support to find the optimal parameters | [42] |
 LR-VAE | Logistic Regression with reduced features using VAE (Variational AutoEncoder) | |
 LRCV-VAE | LRCV-VAE: Logistic Regression with build-in validation support to find the optimal parameters and reduced features using VAE | |
Nearest neighbors | ||
 KNN | k-nearest neighbors algorithm that predicts the class of object to the class of most common among its k nearest neighbors | [45] |
Support vector machine | ||
 SVC | C-Support Vector Classification is a method for classification by constructing a set of hyperplanes in high dimensional space | [46] |
Ensemble methods | ||
 AdaBoost | AdaBoost algorithm is an iterative procedure that tries to approximate the Bayes classifiers by combining many weak classifiers | |
 GTB | Learning procedure in Gradient Tree Boosting consecutively fit new models to provide a more accurate estimate of the response variable | |
 RF | Random forest is an ensemble classifier by constructing many decision trees and the final prediction is selected by most trees | [51] |
 Bagging | Bagging algorithm is a method for generating multiple versions of a predictor, then using these predictions to get an aggregated predictor | [52] |
 Ensemble | Aggregate the predictions of all other classifiers together. The continuous probability of a subject being asthmatic is the average probabilities of 15 methods, and a subject is predicted as asthmatic if it was predicted as asthmatic by at least 7 methods |  |
Decision trees | ||
 DecisionTree | Decision Trees predict the response value by learning simple decision rules inferred from the data features | [53] |
 ERT | An extremely randomized tree classifier is a tree-based ensemble method consisting of randomizing strongly both attribute and cut point choice | [54] |
Naïve Bayes | ||
 BernoulliNB | Implements the Naïve Bayes training and classification for data that is distributed based on multivariate Bernoulli distribution | [55] |
 GaussianNB | Implements the Naïve Bayes training and classification for data that is distributed based on multivariate Gaussian distribution | [56] |
Neural networks | ||
 MLP | Multi-layer Perceptron in a fully connected feedforward neural networks with at least three layers | [57] |
 MOGONET | MOGONET is a multi-omics data analysis framework for classification tasks utilizing graph convolutional networks | [14] |
 Tabnet | Tabnet uses a canonical deep neural networks architecture for tabular data with interpretability | [21] |