Hello,
everyone,
and
welcome
to
our
presentation.
My
name
is
Dr
Andy Mauromoustakos.
I'm
a
professor
at
the
Agricultural
Statistics
Lab
at
the
University
of
Arkansas.
My
co-
presenter
is
Dr
Shawna
Weimer
. She's A
ssistant
Professor
at
Poultry
Science
Department
at
the
University
of
Arkansas,
and
she's
the
Animal
Welfare
chairperson
at
the
university.
We're
going
to
talk
to
you
today
about
predictive
models
for
BCO
lameness
using
health
and
stress
and
leg
health
parameters
in
broilers.
Our
presentation
is
going
to
be
short.
We're
going
to
discuss
the
models
that
we
are
fitting,
the
champion
models,
the
ones
that
they
get
the
medals.
We're
going
to
evaluate
them,
and
we're
going
to
have
some
conclusions
in
the
end.
Shawna?
All
right.
This
study
compared
physiological
and
non-invasive
measures
of
stress
and
lameness
in
clinically
healthy
and
BCO—
or
b
acterial chondronecrosis with osteomyelitis—laden
broilers.
BCO
is
a
leading
cause
of
infectious
lameness
in
broiler
chickens,
with
flock
diagnosis
requiring
euthanasia.
Thus,
there's
a
need
for
technological
innovations
to
detect
health
and
lameness
status
in
animals
to
project
the
disease
likelihood
prior
to
clinical
onset.
In
this
study,
birds
were
raised
in
separate
environmental
chambers
with
either
wood
shavings
on
the
floor,
or
the
litter,
and
a
wire
flooring
model
that
is
validated
to
induce
BCO
lameness.
Nine
non-invasive
measures
of
stress
and
lameness
were
collected
from
256
birds,
male
broilers,
over
several
weeks,
which
included
core
body
temperature,
stress
hormone,
surface
temperatures
of
the
head,
the
eye,
and
the
beak,
and
the
legs,
the
hocks,
the
shank,
and
the
feet,
with
infrared
thermography.
Leg
blood
oxygen
saturation
was
also
measured
with
a
pulse
oximeter.
Of
these
measures,
two we
sought
to
validate.
The
first
was
extraction
of
corticosterone
from
the
feathers.
Corticosterone is
the
major
primary
stress
hormone,
and
the
gold
standard
for
measures
is
blood
serum
concentrations,
which
requires
the
capture
and
restraint
of
the
bird
to
collect
it.
So
if
feather
corticosterone
could
be
validated,
then
we
could
simply
clip
a
feather
and
not
put
the
bird
through
that
stress
of
restraint
and
blood
draw.
The
second
was
the
thermal
images,
of
which
each
pixel
has
its
own
temperature
recorded
and
can
be
used
to
quantify
external
changes
in
skin
temperature
related
to
blood
flow,
offering
a
non-invasive
tool
to
measure
health
and
welfare.
During
stress,
peripheral
blood
is
shunted
to
the
core,
and
we
expected
the
average
pixels
of
the
eye
and
the
beak,
or
Eavg
and
Bavg,
to
be
lower
in
lame
than
sound
birds,
which
we
correlated
with
the
serum
corticosterone.
For
the
thermal
images
of
the
legs,
we
expected
the
average
pixel
temperature
of
the
hock,
the
shank,
and
the
foot
to
be
lower
in
the
lame
birds,
both
for
the
stress
reasons
and
also
for
the
colonization
of
the
bacteria
that
slowed
the
blood
flow,
which
we
correlated
with
the
leg
blood
oxygen
saturation.
There
were
marked
differences
between
lame
and
sound
birds.
Our
objectives
is
to
identify
which
of
these
nine
health
and
stress
indicators
are
important
for
lameness.
We
want
to
build
models,
both
for
prediction
purposes,
but
we
are
in
agriculture,
and
we
like
to
publish
papers
that
try
to
explain
how
our
inputs
are affect
the
response.
We
are
hoping
that
some
of
the
models,
the
traditional
regression
models,
will
do
fairly
well
and
we
can
interpret
those.
In
our
methods,
we
talked
about…
It's
a
balanced
study.
It's
a
match paired experiment
where
every
time
a
sound
bird
is
observed,
a
lame
bird
is
also
observed
in
the
same
indicators.
We
have
an
incidence
of
a
disease
of
0.5.
We're
going
to
take
advantage
of
the
Model
Screening
platform
of
JMP
Pro
17.
We're
going
to
select
the
lame
that
has
values
1
and
0
categorical.
1 stands
for,
yes,
it's
lame. 0
is
the
sound
.
We
have
our
nine
predictors
in
here.
We're
going
to
do
the
defaults.
We're
going
to
fit all
of
the
machine
learning
models,
that
they
are
checked.
We're
going
to
select
cross
validation.
We're
going
to
have
5-fold
cross
validation.
With
our
approximate
250
birds,
we're
going
to
have
about
50
birds
per
fold,
and
we're
going
to
repeat
it
twice.
We
selected
the
random
seed
so
we
can
reproduce
the
results.
Notice
that
I
did
not
add
the
quadratic
terms
and
interaction
terms
to
hopefully
have
easier
interpretations.
When
we
select
this,
and
we
click
"
Run
,"
JMP
takes
about
a
minute. I
t's
going
to
come
up
with
the
ranking
of
the
models
that
we
have
fitted.
This
is
what
we
call
the
beginning
of
the
end.
We
have
10
different
data
sets
that
we
have
tried.
This
is
the
average
fit
criteria,
higher
the
RS
quare, and
is
the
ranking
the
best,
second
best,
third
best.
Hopefully
for
us,
we
expected
that
the
Neural Boosted
model
may
do
a
little
bit
better
than
the
traditional
regression
models,
such
as
the
Penalized
Regression
and
the
Logistic
Regression
models,
but
we
were
happy
to
see
that
these
are
our
close
second.
If
we
wanted
to
see
how
our
best
model
did,
we're
going
to
go
and
see
that
the
best
model—
the
Neural Boosted,
that
is
the
best
model
for
predicting
purposes
—had
a
misclassification
of
0.
You
can
see
here
both
in
the
training
and
the
validation
that
our
receiver
operating
characteristic
curve
reaches
very
soon
the
1
and
stays at
1,
which
is
extremely
good.
We
see
the
confusion
metrics
that
out
of
the
49
birds,
we're
not
really
misclassifying
any
of
them, so
this
is
a
very
good
model
with
a
very
high
generalized
RS
quare
and
all
of
the
other
fit
criteria
that
is
produced
in
here.
But
we
are
more
interested
in
the
regression
type of
models.
The
regression
type
of
models,
the
Generalized
Regression,
we
can
see
that
when
we
use
Lasso
as
the
estimation
method,
the
model
included
all
of
the
variables,
and
we
have
couple
of
non
-significant
variables
in
the
model
in
here.
We
can
see
these
non
-significant
factors,
such
as
the
FCORT,
you
can
see
it
that
it's
not
crossing
the
horizontal
line.
You
can
see
that
the
year
average
is
not
significant,
but
is
included.
We
can
see
that
this
model
had
approximately
three misclassification, a misclassification
rate
of
about
7
%.
Here
is
the
third
model
that
was...
If
we
had
to
give
it
gold,
silver,
and
bronze,
these
two
will
share
the
second
place.
The
Logistic
Regression
model,
when
we
did
the
step
wise
procedure,
decided
not
to
include
the
two
highly
non
-significant
factors.
We
can
see
in
our
regression
model,
in
the
Logistic
Regression
model,
that
SCORT
is
the
most
important
variable.
This
model
has
similar
misclassification
rate
of
three,
the
same
as
the
Logistic
Regression.
Here
is
how
the
seven
indicator
variables
are
used
to
predict
the
probability
of
lameness.
What
we
like
about
the
regression
type
of
models
is
that
we
can
get
odds
ratios
that
will
help
us
to
interpret.
For
example,
for
our
most
important
variable,
we
can
see
that
the
odds
of
lameness
is
twice
with
one
unit
of
increase
is
serum
cortisol.
Overall,
we
would
like
to
say
that
the
Logistic
Regression
model
only
used
seven
out
of
the
nine
indicators.
The
Neural Boosted
model
and
the
Generalized
Regression,
both
of
them
used
all
nine
indicators
for
lameness.
All
of
the
models
have
area
under
the
curve of
greater
than
0.9. All
of
the
models
have
a
lower…
The regression
type
of models
had
the
7
%
misclassification
on
the
validation
set,
and
the
Neural Boosted
did
not
have
anything.
We
can
compare
our
three
winner
models
using
the
Model
Comparison
platform
in
JM
P.
We
can
see
that
in
terms
of
predicting,
strictly
predicting,
the
Neural Boosted
model
is
significantly
better
than
both
of
the
Generalized
Regression
and
the
regression
model.
The
two
regression
models
,
the
one
with
all
nine
variables versus
the
one
with
the
seven
variables,
are
not
significantly
different
from
each
other.
They
all
had
a
very
similar
area
under
the
curve,
and
they
had
similar
misclassification
of
three
birds.
This
is
our
presentation.
We'd
like
to
thank
you
for
your
attention.
We
have
some
references
that
you
can
find
similar
related
material
to
the
techniques
that
we
used
in
JMP
documentation
that
will
help
you
through
this
process.
Thank
you
for
your
attention.
