Personalized medicine for patients with COPD: where are we?

Figures & data

Table 1 Overview of the reviewed publications focusing on machine learning in clinical decision support for COPD

Aim	Type of data	ML algorithms	Source
Diagnosis (and classification) of COPD	Pulmonary function tests	LM, KNN, NB, DTREE, RF, GB, SVM, ANN, neuro-fuzzy	^Citation154–Citation160
	CT images	LM, KNN, SVM	^Citation161–Citation167
	Breath analysis	LM, KNN, NB, DTREE, RF, SVM, ANN, rule-based, ensembles	^Citation168–Citation171
	Patient health status	ANN, AIS	^Citation146,Citation147
Detection of emphysema	Images (CT & radiography)	LM, KNN, NB, DTREE, SVM, ANN	^Citation172–Citation179
Prediction of exacerbations	Respiratory sounds	RF, SVM	^Citation180,Citation181
	Patient health status	NB, BN, DTREE, RF, SVM, ANN	^Citation145,Citation148–Citation152,Citation182

Abbreviations: AIS, artificial immune system; ANN, artificial neural network; BN, Bayesian network; DTREE, decision tree; GB, gradient boosting; LM, linear models; KNN, k-nearest neighbor; ML, machine learning; NB, Naïve Bayes; RF, random forest; SVM, support vector machine.

Figure 1 Different machine learning algorithms. (A) k-nearest neighbor (KNN); (B) artificial neural network (ANN); (C) support vector machine (SVMs). Different algorithms are explained in the“Machine learning models” section.

Notes: Class represents diagnostic classification, for example as 'normal' or 'abnormal' or representing different stages of disease. Test represents new cases entering classification.

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