Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Multilabel 12-Lead Electrocardiogram Classification Using Gradient
  Boosting Tree Ensemble   The 12-lead electrocardiogram (ECG) is a commonly used tool for detecting
cardiac abnormalities such as atrial fibrillation, blocks, and irregular
complexes. For the PhysioNet/CinC 2020 Challenge, we built an algorithm using
gradient boosted tree ensembles fitted on morphology and signal processing
features to classify ECG diagnosis.
  For each lead, we derive features from heart rate variability, PQRST template
shape, and the full signal waveform. We join the features of all 12 leads to
fit an ensemble of gradient boosting decision trees to predict probabilities of
ECG instances belonging to each class. We train a phase one set of feature
importance determining models to isolate the top 1,000 most important features
to use in our phase two diagnosis prediction models. We use repeated random
sub-sampling by splitting our dataset of 43,101 records into 100 independent
runs of 85:15 training/validation splits for our internal evaluation results.
  Our methodology generates us an official phase validation set score of 0.476
and test set score of -0.080 under the team name, CVC, placing us 36 out of 41
in the rankings.

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The categories of this paper are: atom-ph
7
The categories of this paper are: hep-th
17
The categories of this paper are: q-fin
35
The categories of this paper are: mtrl-th
6
The categories of this paper are: astro-ph
5
The categories of this paper are: gr-qc
21
The categories of this paper are: adap-org
0
The categories of this paper are: hep-ph
20
The categories of this paper are: acc-phys
31
The categories of this paper are: ao-sci
15
The categories of this paper are: nucl-ex
37
The categories of this paper are: supr-con
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The categories of this paper are: cmp-lg
3
The categories of this paper are: physics
27
The categories of this paper are: comp-gas
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The categories of this paper are: chao-dyn