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Constructing and Visualizing Models using Mime-based Machine-learning Framework
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Artificial-Intelligence-Based Models Coupled with Correspondence Analysis Visualization on ART-Cases from Gombe

Kabiru Bala1,2, Ilker Etikan1, A G Usman3,4

  • 1Biostatistics Department, Faculty of Medicine, Near East University, 99138 Nicosia, Cyprus.

Life (Basel, Switzerland)
|March 29, 2023
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Summary

Antiretroviral therapy (ART) improves outcomes for HIV/AIDS patients. This study shows ART medication increases life expectancy and identifies key patient factors associated with treatment success.

Keywords:
Gombe stateNigeriaantiretroviral therapyartificial intelligencecorrespondence analysisprotease inhibitors

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Area of Science:

  • Medical Informatics
  • Public Health
  • Artificial Intelligence in Medicine

Background:

  • Antiretroviral therapy (ART) is crucial for managing HIV/AIDS, but patient commitment and community support remain significant challenges.
  • Understanding treatment progress is vital for optimizing patient outcomes and resource allocation in HIV/AIDS care.

Purpose of the Study:

  • To investigate and predict the progress of ART in HIV/AIDS patients at a Nigerian Federal Teaching Hospital.
  • To evaluate the efficacy of various artificial intelligence (AI) models and correspondence analysis (CA) in visualizing ART treatment trajectories.

Main Methods:

  • Employed artificial intelligence (AI) models including artificial neural networks (ANNs), adaptive neuro-fuzzy inference systems (ANFISs), support-vector machines (SVMs), and multiple linear regression (MLR).
  • Utilized correspondence analysis (CA) and chi-square tests to analyze patient data and visualize associations between ART drugs and demographic variables.
  • Data was sourced from patient records within the ART unit of the Federal Teaching Hospital, Gombe State, Nigeria.

Main Results:

  • The ANFIS model demonstrated superior performance in both training and testing phases, with high R-squared values (0.903 and 0.904) and low Mean Squared Error (MSE).
  • Findings indicate that increased duration of ART medication is associated with improved safety and longevity for HIV/AIDS patients.
  • Correspondence analysis revealed a significant association between ART drugs and patient age group, with marital status explaining a substantial 93.7% of the variation in treatment progress.

Conclusions:

  • ANFIS is a highly effective AI model for predicting ART treatment progress in HIV/AIDS patients.
  • Long-term adherence to ART medication significantly contributes to better health outcomes and extended lifespans for individuals living with HIV/AIDS.
  • Demographic factors, particularly marital status and age, play a crucial role in the effectiveness and visualization of ART treatment success.