Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Smartphone-Based Test and Predictive Models for Rapid, Non-Invasive, and
  Point-of-Care Monitoring of Ocular and Cardiovascular Complications Related
  to Diabetes   Among the most impactful diabetic complications are diabetic retinopathy, the
leading cause of blindness among working class adults, and cardiovascular
disease, the leading cause of death worldwide. This study describes the
development of improved machine learning based screening of these conditions.
First, a random forest model was developed by retrospectively analyzing the
influence of various risk factors (obtained quickly and non-invasively) on
cardiovascular risk. Next, a deep-learning model was developed for prediction
of diabetic retinopathy from retinal fundus images by a modified and re-trained
InceptionV3 image classification model. The input was simplified by
automatically segmenting the blood vessels in the retinal image. The technique
of transfer learning enables the model to capitalize on existing infrastructure
on the target device, meaning more versatile deployment, especially helpful in
low-resource settings. The models were integrated into a smartphone-based
device, combined with an inexpensive 3D-printed retinal imaging attachment.
Accuracy scores, as well as the receiver operating characteristic curve, the
learning curve, and other gauges, were promising. This test is much cheaper and
faster, enabling continuous monitoring for two damaging complications of
diabetes. It has the potential to replace the manual methods of diagnosing both
diabetic retinopathy and cardiovascular risk, which are time consuming and
costly processes only done by medical professionals away from the point of
care, and to prevent irreversible blindness and heart-related complications
through faster, cheaper, and safer monitoring of diabetic complications. As
well, tracking of cardiovascular and ocular complications of diabetes can
enable improved detection of other diabetic complications, leading to earlier
and more efficient treatment on a global scale.

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The categories of this paper are: cmp-lg
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The categories of this paper are: supr-con
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The categories of this paper are: nucl-ex
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The categories of this paper are: plasm-ph
2
The categories of this paper are: math
6
The categories of this paper are: astro-ph
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The categories of this paper are: chem-ph
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The categories of this paper are: dg-ga
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The categories of this paper are: nlin
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The categories of this paper are: cond-mat
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The categories of this paper are: bayes-an
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The categories of this paper are: hep-lat
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The categories of this paper are: q-fin
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The categories of this paper are: ao-sci
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The categories of this paper are: acc-phys
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The categories of this paper are: alg-geom