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ORIGINAL RESEARCH article

Front. Nutr.
Sec. Nutritional Immunology
Volume 11 - 2024 | doi: 10.3389/fnut.2024.1443076
This article is part of the Research Topic Micronutrients, Immunity and Infection View all 23 articles

Predicting Immune Risk in Treatment Naïve HIV patients by Machine Learning Modalities: A Decision Tree algorithm based on Micronutrients and Inversion of CD4/CD8 Ratio

Provisionally accepted
Saurav Nayak Saurav Nayak Arvind Singh Arvind Singh Manaswini Mangaraj Manaswini Mangaraj GAUTOM K. SAHARIA GAUTOM K. SAHARIA *
  • All India Institute of Medical Sciences Bhubaneswar, Bhubaneswar, India

The final, formatted version of the article will be published soon.

    Introduction: Micronutrients have significant functional implications for the human immune response, and the quality of food is a major factor of the severity and mortality of HIV in individuals on antiretroviral therapy. A fall in CD4 lymphocyte numbers and a spike in CD8 lymphocyte counts are the hallmarks of HIV infection, which causes the CD4/CD8 ratio to invert from a normal value of > 1.6 to < 1.0. In this study we are trying to analyse whether the nutrient status of the individual have an impact on the CD4/CD8 ratio inversion by utilising Machine Learning algorithms.Method: Fifty-five confirmed HIV positive patients who have not started with anti-retroviral therapy were included in the study after getting informed written consent from the participants.Fifty-five age and sex-matched, relatives, and attendants of patients found negative in screening test were enrolled as controls. All individual patient datapoints were analysed for developing the models with an 80-20 train-test split. The four trace elements, Zn, P, Mg and Ca were utilized by implementing Random Forest Classifier. The targets were inverted CD4/CD8 Ratio.Result: A total of 110 participants' data were included in the analysis. The algorithm thus generated had a Sensitivity of 80% and Specificity of 83%, with LR+ of 4.8 and LR-of 0.24.The utilization of ML algorithm boosts the narrow evidence that exists currently regarding the role of micronutrients especially trace elements in the causation of immune risk. Being one of the first studies of this kind that utilized ML based Decision Tree algorithm to classify immune risk in HIV patients is inherently our strength.Our study uniquely corroborates nutritional data to immune risk in treatment naïve HIV patients through the utilization of Decision Tree ML algorithms. This will subsequently be an important classification and prognostic tool in the hands of clinicians.

    Keywords: HIV, Micronutreints, machine learning and AI, CD4 /CD8 lymphocytes + +, Treatment naive patients, Zinc, Calcium, Magnesium

    Received: 03 Jun 2024; Accepted: 11 Sep 2024.

    Copyright: © 2024 Nayak, Singh, Mangaraj and SAHARIA. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

    * Correspondence: GAUTOM K. SAHARIA, All India Institute of Medical Sciences Bhubaneswar, Bhubaneswar, India

    Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.