  This paper considers the use of machine learning algorithms for predicting
cocaine use based on magnetic resonance imaging (MRI) connectomic data. The
study utilized functional MRI (fMRI) and diffusion MRI (dMRI) data collected
from 275 individuals, which was then parcellated into 246 regions of interest
(ROIs) using the Brainnetome atlas. After data preprocessing, the datasets were
transformed into tensor form. We developed a tensor-based unsupervised machine
learning algorithm to reduce the size of the data tensor from $275$
(individuals) $\times 2$ (fMRI and dMRI) $\times 246$ (ROIs) $\times 246$
(ROIs) to $275$ (individuals) $\times 2$ (fMRI and dMRI) $\times 6$ (clusters)
$\times 6$ (clusters). This was achieved by applying the high-order Lloyd
algorithm to group the ROI data into 6 clusters. Features were extracted from
the reduced tensor and combined with demographic features (age, gender, race,
and HIV status). The resulting dataset was used to train a Catboost model using
subsampling and nested cross-validation techniques, which achieved a prediction
accuracy of 0.857 for identifying cocaine users. The model was also compared
with other models, and the feature importance of the model was presented.
  Overall, this study highlights the potential for using tensor-based machine
learning algorithms to predict cocaine use based on MRI connectomic data and
presents a promising approach for identifying individuals at risk of substance
abuse.
