An Integrated Machine Learning Approach

zoomzurichAI and Robotics

Oct 16, 2013 (3 years and 10 months ago)

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Intelligent Database Systems Lab

N.Y.U.S.T.

I. M.

An Integrated Machine Learning Approach

to Stroke Prediction

Presenter: Tsai
Tzung

Ruei


Authors:
Aditya

Khosla
, Yu Cao, Cliff
Chiung
-
Yu Lin, Hsu
-
Kuang

Chiu,
Junling

Hu,
Honglak

Lee


SIGKDD 2010

國立雲林科技大學

National Yunlin University of Science and Technology

Intelligent Database Systems Lab

N.Y.U.S.T.

I. M.

Outline


Motivation


Objective


Methodology


Experiments


Conclusion


Comments

2

Intelligent Database Systems Lab

N.Y.U.S.T.

I. M.

Motivation


Most
previous prediction models
have adopted
features (risk
factors) that are verified by clinical trials or
selected
manually

by
medical experts
.


In
the past,
high
-
performance machine learning
algorithms
such as
SVM

and
logistic regression
were
not explored
.



3

Intelligent Database Systems Lab

N.Y.U.S.T.

I. M.

Objective


To
propose
a
novel

automatic feature selection
algorithm that selects
robust

features based on our
proposed heuristic:
conservative mean
.


To present a
margin
-
based censored regression
algorithm
that combines the concept of margin
-
based
classifiers with censored regression to
achieve a better
concordance index

than the Cox model.


4

Intelligent Database Systems Lab

N.Y.U.S.T.

I. M.

Methodology


5

Intelligent Database Systems Lab

N.Y.U.S.T.

I. M.

Methodology


Conservative mean feature selection


To consider the
variance

across
different folds
along with the
average
of the prediction performance.


To evaluate the performance

of
each feature
individually.

6

Age

Calculated
hypertensi
on status

Left
ventricular
mass

Intelligent Database Systems Lab

N.Y.U.S.T.

I. M.

Methodology


Conservative mean feature
selection

7

VECTOR




Age

Left
ventricular
mass

Calculated
hypertension
status

Intelligent Database Systems Lab

N.Y.U.S.T.

I. M.

Methodology


Learning Algorithms for Prediction


Margin
-
based Censored Regression



8

SVM

True

False

Intelligent Database Systems Lab

N.Y.U.S.T.

I. M.

Experiments


Data
Imputation





Feature
Selection

9

Intelligent Database Systems Lab

N.Y.U.S.T.

I. M.

Experiments


Stroke Prediction

10

Intelligent Database Systems Lab

N.Y.U.S.T.

I. M.

Experiments


Identifying risk factors

11

Intelligent Database Systems Lab

N.Y.U.S.T.

I. M.

Conclusion


Contribution



An
extensive evaluation
of the problems of
data
imputation
,
feature selection

and
prediction in
medical data
, with comparisons against the
Cox
proportional hazards

model
.


A
novel

feature selection
algorithm,
Conservative
Mean feature
selection
, that outperforms both L 1
regularized
Cox
model and L 1 regularized logistic regression on
the CHS
dataset
.


A
novel risk prediction
algorithm,
Margin
-
based
Censored Regression
, that outperforms the Cox model given

the
same set of features.


12

Intelligent Database Systems Lab

N.Y.U.S.T.

I. M.

Comments


Advantage


The structure of this paper is very clear.


Drawback


……


Application


classification




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