Hello.
Good
morning,
everyone.
This
is
Saijac Lami
and
I
have
my
teammate,
Zhe Diao.
Basically,
we
are
a
business
analytics
graduate
students
from
University of Connecticut-Stamford Campus.
Little
about
our exposure
to
JMP.
We
have
extensively
used
JMP
in
our P
rediction Model
course
in
our
first semester
.
We
felt
it's
a
very
easy
and
very
powerful
tool,
and
there
is
a
lot
we
can
do
from
it.
We
are
still
exploring
the
many
features
of
the
JMP.
Today,
we
are
here
to
present our work of which we did during summer
that is a heart
failure
prediction
using
modern
screening
platform.
We
are
calling
it
improved
because
we
use
several
JMP platforms
to
leverage
predictions.
Coming
to
the
agenda
today,
so
this
is
just
an
overview
slide
from
which
you
would
get
the
gist
of
what
we
are
doing,
we
talked
about
followed
by
the
three
slides,
which
we
where
we
talk
about
pre-processing
and
some
EDA,
which
we
have
done,
and
the
modeling.
Coming
to
the
introduction,
we
know
that
cardiovascular disease
is
the
number
one
causing
of
the
death
globally,
claiming
an
estimated
of
70.9
billion lives
in
2019.
It
accounts
for
around
32
%
of
deaths
worldwide
every
year.
In
our
problem,
so
we
have
taken
a gathered
data
set,
and
we
developed
a
classification
model
for
classifying
the
heart
disease.
We
also
leverage
this
predictions
using
the
model
screening,
modal
comparison
and
attachment
feature
in
the
JMP
16.
The
model
output
will
help
in
understanding
the
importance
of
factors
that
are
leading
to
heart
disease
and
the
probability.
We
also
find
the
probability
of
developing
the
heart
disease,
unless when under
c
ertain
conditions.
Summarizing
our
objective
is
to
build
the
best
model
and
find
the
factors
that
are
leading
to
the
heart
failure
using
the
JMP 16
platform.
Coming
to
a
methodology
and
little
about
our
data
set,
the
data
set
included
around
918
participants
from
different
countries.
There
are
11
factors
associated
to
heart
failure,
such
as
age,
sex,
blood
pressure,
and
blood
glucose.
How we went
to
our
predictions
is
we
first perform
the
pre-processing
of
the
data
by
exploring
if
there
are
any
missing
values
or
any
outliers.
In further,
we've
performed
the
EDA
analysis
to
understand
the
importance
and
the
relationship
of
the
each
feature
in
relation
to
the
heart
failure.
To
build
the
model,
we
incorporated
the
following JMP
16
capabilities
in
our
methodology.
The
first thing
is
the
Model
Screening.
Which
is
an
efficient
platform,
a
simultaneously
fitting,
comparing
and
exploring,
selecting
and
then
deploying
the
best
predictive
model.
The
next
comes
the
Model
Comparison,
which
an
easy
platform
to
compare
and
select
the
best-
performing
predictive
model.
Next
comes the dashboard
is
an
efficient
way
to
better
represent our
EDA
concisely.
We
can
run
any
time
as
the
new
data
is
available.
I'm
coming
to
our
results.
This
is
just
overview
of
the
results
which
we
had.
Using
the
model
screening,
we
identified
that
the
Boosted
Tree
is
our
best
model.
We
choose
and
we
have
also
not
just
on
accuracy.
We
focus
on
which
is
having
the
least
one
rate,
because
we
do
not
want
our
model
to
have
high
false
positives,
because
we
don't
want
the
heart
rate,
like
heart
failure
predict
patients
not
to
work
as
not
detected.
Based on
this,
we
have
chosen
the
Boosted
Tree
as
our
best
model.
Coming
to
the
column
contributions,
so
when
we
are,
when we
tried
to
identify
what
are
the
important
factors
that
are
causing
the
heart
failure
prediction.
When using
the
Boosted
Tree,
we
identified
that
Exercise Angina,
so
which
is
if
a
person
has
the
in-
use
pain
due
to
exercise
and
also
Fasting
Bs, Blood Glucose level,
Resting ECG, ST_slope, Chestpain type
are few parameters out of 11,
are contributing to around 75% of
the heart failure.
Next comes in more depth analysis,
Zhe Diao will
be
taking
care.
Okay, after
screening
through
the
basic
information
of
the
data,
such
as
target
feature,
predictive
variables,
data
types,
our
analysis
work
will
start
with
data
cleaning.
We
need
to
deal
with
missing
values
and
all
layers
first to
get
a
clean
data.
JMP
provides
a
variety
of
ways
to
explore
and
deal
with
them.
For
the
missing
value,
JMP can
display
the
details
in
the
summary
table
are rarely
display
them
in
cell
plot
or
tree
map.
Today,
we
show
the
statistical
table
here,
which
is
also
a
way
to
get
the information
we
want.
We
can
see
that
there
is
no
missing
value
in
our
data.
But
when
you
further
explore
the
data
distribution,
you
will
find
that
some indicators
use
the
number
zero
to
replace
the
missing
value.
We
treat
the
data
zero
with
deletion
and
the
media
replacements
because
the
value
of
the
lead
indicator
cannot
be
zero.
For
the
Outlier,
box plot
and
explore
Outlier module
are
common
methods.
Today,
we
use
the
Outlier Ana
lysis function,
and
there's
a
multi-variate
module,
which
reflect
the
distance
of
a
multi-dimensional
space
into
this
Mahalanobis
Distance
Graph.
We
have
retain
this
Outliers
in this
analysis
because
we
consider
that
this is a
common
phenomenon
in
medical
test
results.
After
completing
these
steps,
we
get
the
clean
data,
and
then
we
enter
the
data
exploration
stage.
In
this
step,
we
built
some
commonly
used
the
charts
to
show
some
information
contained
amount
data.
JMP
provides
us
many
choices
in
this part
such
as
tree
map,
ring
chart,
bar
chart and
zero.
From
these
graphs,
we
know
that
the
proportion
of
male
suffering
from
heart
failure
is
twice
that
are
female.
80
%
of
patients
with
heart
failure
have
diabetes
and
77
persons
have
no
symptoms
of
chest
pain,
which
reveals
the
imperceptibility
of
the
disease.
After
we
draw
this
useful
conclusions,
we
come
to
the
modeling
stage
to
further
explore
the
relationship
between
data.
When
you
are
doing
data
analysis,
you
may
usually
think
what
model
I
want
to
build,
or
what
model
performs
best.
Model
screening
function
in
JMP
helps
us
solve
this
problem
in a
very
intuitive
way.
You
just
need
to
drag
the
target
variable
and
the
prediction
variables
to
the
corresponding
positions.
JMP will
help
you
run
also
appropriate
models.
In
this
analysis,
JMP write
nine
models
automatically,
including
Regression
model,
Boosted Tree, Neural
ne
twork
and
so
on.
You
can
get
a
detailed
and
clear
output.
If
you
only
care
about
the
results,
the
summary
table
can
help
you
choose
the
best
model,
whether
you
consider
residual
or
fitting
degree.
If
you
want
to
know
the
detail,
the
parameters
and
the
results
of
each model,
you
just
need
to
click
the
model
you
want
to
view
in
details
part,
and
you
can
understand
the
performance
of
the
model
from
all
aspects.
Here
we
intercept
a
parameters,
computer
matrix
and
profiler.
In
profiler,
you
can
enter
new
data
to
observe
the
change
train
of
each
variable
and
get
the
predict
the
result.
We
see
that
the
influence
of
age
is
not
significant,
which
may
be
countering
to
our combination.
Where
gender,
diabetes
and ST_
slope
are
the
main
influence
factors.
Moreover,
in
these
results,
we
pay
attention
to
the
misclassification
rate,
especially
is
a
false
negative
value,
because
it
means
that
the
patient
has
heart
failure,
and
we
predict
that
he
does
not,
which
may
lead
to
very
serious
consequence.
The
best
performance
model
we
select in
this
analysis
is
supposed
to
decrease
which
has
the
lowest
the
misclassification
rate
and
the
highest
sensitivities.
Then
we
can
save
all
the
prediction formulas
and
the
results
for
use
in
the
model
comparison.
Model comparison provides
more
concise
and
intuitive
format
to
show
model
performance
indicators,
which
is
convenient
for
us
to
make
the
final
choice.
Now,
I
take
through
to
the
last
part
of
our
presentation,
which
is
the
dashboard.
Using
the
dashboard
feature,
we
created
a
utility
where
we
added
several
important
features,
which
we
discussed
before,
and
which
are
critically
affecting
the
heart
rate.
Here
we
can
interact
by
providing
the
inputs
Like
I
can
choose
male
or
female,
and
we
can
even
choose
the
Chest
pain
type.
Also,
what's
the
ST_s
lope
pattern,
and
also,
what's
the
excess
in
genome?
Based on
this,
I
can
interact
with
the
utility,
and
also
based
on
that,
it
will
display
the
probability
of
the
heart
rate,
which
is
a
pretty
useful
feature.
That
comes
to
the
last
part.
In
conclusion,
I
just
want
to
summarize.
We
use
the
modern
screen
platform,
and
through
which
we
explore
the best predictive
modeling
for
the
heart
failure
prediction.
We
also
leverage
whatever
the
work
we
try using
the
JMP 16
dashboard.
Which
we
develop
a
utility
to
develop
an
interactive
platform
that
outputs
the
probability
of
the
heart
failure
based
on
the
input
and
parameters.
That's
all
we
have
for
today.
Thank
you.
