1. Results Motivation Introduction Methods Conclusions Acknowledgements
Analysis of Mental Impairment in HIV-Perinatally Infected Children using Machine Learning Prediction Model Bhupendra Acharya, Md Tamjidul Hoque
{bacharya,
Department of Computer Science, University of New Orleans, New Orleans, LA, USA
Results
Motivation
According to 2017 data from in Global HIV and AIDS statistics on children about 1.8 million children under 15 years are living with HIV [1]. Worldwide 10-20% of children experience mental disorders [2].
We are motivated to find the results on physiological growth, cognitive reasoning, psychological wellbeing and on children, those were perinatally HIV infected.
Fig 1. Images of Voxel Placement
Description: Sagittal, coronal, and axial views are shown from left to right. Location of (a) mid-frontal gray matter voxel, (b) basal ganglia voxel, and (c) peritrigonal white matter voxel.
Fig2. HIV Prevalence with age group
Description: The result displaying the observed HIV Prevalence in Manicaland and Spectrum projections for all of Zimbabwe for a given age group with HIV occurrence
Introduction
The children living with HIV are presumed to be infected perinatally from mother-to-child transmission (PMCT).
The health and wellbeing of the children depend on the mother’s infection linked to HIV status and survival.
In this study, we look at two aspects:
Physiological Growth and Cognitive reasoning- Analysis of regional age-related changes in metabolite levels of neurodevelopment
Psychological wellbeing - Wellbeing across ages and the association among wellbeing, skills, and survival at ages.
The comparison of children who were infected with HIV and uninfected and their association with mental impairment is shown through a classification problem approach of Support Vector Machine and other classification regression models.
Table1: Comparison of different machine learning methods
Methods
Tests
Algorithm
Precision
Recall
F-Measure
Correctly Classified
Correctly Classified Instances
Brain Metabolism
SVM
0.983
0.992
0.988
99.17%
239 / 241
Logistic
0.985
98.75%
238 / 241
Random Forest
Psychological Wellbeing
0.986
98.61%
3342 / 3389
0.997
0.977
99.70%
3379 / 3389 1
99.97%
3388 / 3389
Datasets Collection
For the physiological growth and cognitive reasoning
The dataset consists of a total of 64 South African children collected in 2017, between age 5 to 12 years that had 29 HIV exposed and uninfected children [4].
The dataset consists of data extracted from Longitudinal Magnetic Resonance (MRI), Magnetic Resonance Spectroscopy (MRS), and Diffusion Tensor Imaging (DT) that shows the brain structure networks and the cognitive and behavioral maturation in the stage of childhood.  This helps us to show brain metabolism in different regions.
We examined age-related trajectories such as Creatine, N-acetyl-aspartate, the combined NAA+N-acetyl-aspartyl-glutamate, choline, glutamate and the combined Glu and glutamine in voxels within gray and white matter, as well as subcortically in the basal ganglia (BG).
For the psychological well being
We used the dataset from Zimbabwe that was collected between 2009 to 2011, children age ranging from 2 to 12 years old of total 3389 [3].
We examined the data with children of different age, weight, height with respect to mother PMTCT, antiretroviral therapy, mother’s abuse on sexual birth, breastfeeding and sickle cell anemia.
Feature construction
For Physiological Growth and Cognitive reasoning, we used 23 features. These include age, gender, HIV exposure, and brain metabolic trajectories that are undergoing during children growth such as Creatine, N-acetyl-aspartate, choline, glutamate, and their combinations.
We used 27 features for Psychological wellbeing dataset. These include age, gender, the school enrolled, psychological wellbeing likelihood tests, weight for age, weight for height, weight for body mass index, mother HIV tests results, cured sickle, mother PMTCT, horizontal risk, taken any antiretroviral during pregnancy, had forced sex with mother, etc.
Machine learning method
We used Support vector machine model, Logistic Regression and Random Forest for the result prediction. It is a supervised learning model that analyzes data for classification and regression analysis. Since our analysis is based on whether a child with HIV infected is likely to have a mental impairment or not, we used SVM.
The classification approach is done in 10-Fold cross-validation using SVM of Y-Predicted value. The supervised model of Y value is manually entered in the dataset.
For Psychological wellbeing, the prediction value was based on the log-likelihood of Psychological wellbeing tests.
For Physiological Growth and Cognitive reasoning, the predicted value was based on the brain metabolic growth at different trajectories associated to age group.   
Fig 4. Classification Prediction of different features for physiological growth and cognitive reasoning
Fig 3. Classification Prediction of different features for psychological wellbeing
Conclusions
Acknowledgements
Our research from two independent datasets on physiological metabolic activity in the brain during the children grow and psychological wellbeing shows that children perinatally infected with HIV do not have a correlation with mental impairment.
In summary:
We found that age-related trajectories for synaptic activity for learning were functional similar between infected and uninfected children. That shows that there was no correlation of infected vs uninfected children in brain metabolic activity during children growth.
Similarly, our result from Psychological wellbeing analysis showed that children with HIV infected perinatally and uninfected does not have any correlation with being infected or not. Infected children tend to have lower weight, height, and body mass index compared to normal children. The physiological with respect to height, weight, and BMI does not seem to indicate the likelihood of mental impairment result.
We gratefully acknowledge the Louisiana Board of Regents through the Board of Regents
Support Fund, LEQSF ( )-RD-B-07.
References
[1] World Health Organization.
[2] Psychological wellbeing, health, and aging.
[3] Dataset.
[4] Dataset.
[5] Eric L. Pufall, Constance Nyamukapa, et. al HIV in Children in a General Population Sample in East Zimbawe: Prevalence, Causes and Effects.
[6] Martha J. Holmes, Frances C. Robertsob et. al Longitudinal increase of brain metabolite levels in 5-10-year-old children