Buddle - A Deep Learning for Statistical Classification and Regression
Analysis with Random Effects
Statistical classification and regression have been
popular among various fields and stayed in the limelight of
scientists of those fields. Examples of the fields include
clinical trials where the statistical classification of
patients is indispensable to predict the clinical courses of
diseases. Considering the negative impact of diseases on
performing daily tasks, correctly classifying patients based on
the clinical information is vital in that we need to identify
patients of the high-risk group to develop a severe state and
arrange medical treatment for them at an opportune moment. Deep
learning - a part of artificial intelligence - has gained much
attention, and research on it burgeons during past decades:
see, e.g, Kazemi and Mirroshandel (2018)
<DOI:10.1016/j.artmed.2017.12.001>. It is a veritable technique
which was originally designed for the classification, and
hence, the Buddle package can provide sublime solutions to
various challenging classification and regression problems
encountered in the clinical trials. The Buddle package is based
on the back-propagation algorithm - together with various
powerful techniques such as batch normalization and dropout -
which performs a multi-layer feed-forward neural network: see
Krizhevsky et. al (2017) <DOI:10.1145/3065386>, Schmidhuber
(2015) <DOI:10.1016/j.neunet.2014.09.003> and LeCun et al.
(1998) <DOI:10.1109/5.726791> for more details. This package
contains two main functions: TrainBuddle() and FetchBuddle().
TrainBuddle() builds a feed-forward neural network model and
trains the model. FetchBuddle() recalls the trained model which
is the output of TrainBuddle(), classifies or regresses given
data, and make a final prediction for the data.
