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. 2023 Jan 23;15(2):316. doi: 10.3390/v15020316

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© 2023 by the authors.

Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).

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Analyses of variables predicting clinical group classification by machine learning. The regularized logistic regression machine learning method was used to predict variables that distinguished the HIV[+] group from the HIV[−] group (A). Shrinkage discriminant analysis predicted the variable importance for the HIV[+] and HIV[+]/HAND group classification (B). An importance value of 70 was set as a threshold. An importance value between 70 and 100 indicates a heavy reliance of machine learning prediction on a specific variable in classifying and distinguishing the compared groups. (* represents CSF or plasma viral load, and ** represents brain viral load (RNA, DNA or iDNA (integrated DNA).) Host genes were derived from qRT-PCR results.

Analyses of variables predicting clinical group classification by machine learning. The regularized logistic regression machine learning method was used to predict variables that distinguished the HIV[+] group from the HIV[−] group (A). Shrinkage discriminant analysis predicted the variable importance for the HIV[+] and HIV[+]/HAND group classification (B). An importance value of 70 was set as a threshold. An importance value between 70 and 100 indicates a heavy reliance of machine learning prediction on a specific variable in classifying and distinguishing the compared groups. (* represents CSF or plasma viral load, and ** represents brain viral load (RNA, DNA or iDNA (integrated DNA).) Host genes were derived from qRT-PCR results.