A comparative study of logistic regression based machine learning techniques for prediction of early virological suppression in antiretroviral initiating HIV patients.

Journal: BMC medical informatics and decision making
PMID: 30180893

Abstract

BACKGROUND: Treatment with effective antiretroviral therapy (ART) lowers morbidity and mortality among HIV positive individuals. Effective highly active antiretroviral therapy (HAART) should lead to undetectable viral load within 6 months of initiation of therapy. Failure to achieve and maintain viral suppression may lead to development of resistance and increase the risk of viral transmission. In this paper three logistic regression based machine learning approaches are developed to predict early virological outcomes using easily measurable baseline demographic and clinical variables (age, body weight, sex, TB disease status, ART regimen, viral load, CD4 count). The predictive performance and generalizability of the approaches are compared.

Authors

  • Kuteesa R Bisaso
    Infectious Diseases Institute, College of Health Sciences, Makerere University, Kampala, Uganda; Department of Pharmacology and Therapeutics, College of Health Sciences, Makerere University, Kampala, Uganda; Breakthrough Analytics Limited, Kampala, Uganda. Electronic address: kuteesar@gmail.com.
  • Susan A Karungi
    Department of Pharmacology and Therapeutics, College of Health Sciences, Makerere University, Kampala, Uganda.
  • Agnes Kiragga
    Data Science Program, Africa Population Health Centre, Nairobi, Kenya.
  • Jackson K Mukonzo
    Department of Pharmacology and Therapeutics, Makerere University College of Health Sciences, Kampala, Uganda.
  • Barbara Castelnuovo
    Infectious Diseases Institute, College of Health Sciences, Makerere University, Kampala, Uganda.

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