Download Artificial intelligence analysis using Neural Network

Artificial intelligence analysis using Neural Network to predict three stroke parameters: Surgery
needed, Treatment, and Length of Stay for Rehabilitation by
Jorge Garza-Ulloa
Artificial intelligence analysis using Neural Network to predict three stroke parameters:
Surgery needed, Treatment, and Length of Stay for Rehabilitation.
By: Jorge Garza-Ulloa
Electrical & Computer Engineering Doctoral Program
The University of Texas at El Paso
[email protected]
Abstract:
Using Artificial Intelligence tools to predict three Stroke Variables: Surgery needed,
Rehabilitation and Days of rehabilitation, with this information we will have a
reference point and our general goal on future research is to a follow-up of the
subjects to integrate them faster to their normal life to the patients and lower cost
related for expenses of the illness”. Neural Network (NN) algorithms are proposed to
develop a Database Acknowledgment (DBK) to predict the three parameters
mentioned: Surgery, Treatment and Days of rehabilitation. Goal is to find an
optimal Neural network configuration using with the actual information available
using three different software available: one manual (with no automatic stepwise
functions, limited diagnostic), another semi-automatic (allows stepwise function and
good diagnostic) and one Neuro-Intelligence (use Genetic Algorithm to find the best
NN configuration and an excellent diagnostic tools) our proposed solution must be
minimum possible prediction error. Based on the 14 Stroke input variables and the 3
output target Stroke values, this paper suggest that the forecasting of: Surgery,
Rehab and Days of Rehabilitation it is possible using Neural network tools.
Introduction:
“Every year, more than 750,000 people suffer a stroke, a “brain attack”.
Stroke occurs when blood flow to an area of the brain is stopped. As a result, lifesupporting supplies of blood and oxygen are cut off from the brain. Brain damage
due to a stroke can affect important areas that control everything we do – including
how we move different parts of our body.
”The recovery of Stroke Patient’s is regularly very slow and expensive; any solution
to reduce this time and/or prevent this illness will be very usefully for Human
kindness”.
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Artificial intelligence analysis using Neural Network to predict three stroke parameters: Surgery
needed, Treatment, and Length of Stay for Rehabilitation by
Jorge Garza-Ulloa
Neural Network
Neural networks are composed of simple elements operating in parallel. These
elements are inspired by biological nervous systems. As in nature, the network
function is determined largely by the connections between elements. You can train a
neural network to perform a particular function by adjusting the values of the
connections (weights) between elements. Commonly neural networks are adjusted,
or trained, so that a particular input leads to a specific target output.
An elementary neuron with R inputs, where each input is weighted with an
appropriate w. The sum of the weighted inputs and the bias forms the input to the
transfer function f. Neurons can use any differentiable transfer function f to generate
their output.
And get network with the n Input, m Hidden Layers and k Output Target as shown
on the next figure:
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Artificial intelligence analysis using Neural Network to predict three stroke parameters: Surgery
needed, Treatment, and Length of Stay for Rehabilitation by
Jorge Garza-Ulloa
We propose a supervised network that is trained to produce outputs in response to
sample inputs, making them particularly well-suited to modeling and controlling
dynamic systems, and predicting future events.
Dataset for the proposed neural network:
Use dataset available (Collected data) _ The Dataset to use is Stroke_full_database
with 1048 samples: Many Eyes from An experiment brought to you by IBM
Research and the IBM Cognos software [1] :
From this dataset we choose the 14 variables forming 4 different groups:
1) Stroke Independent with three variables : AgeCat ( 4 values form 45 to 86 years
old), Gender and Active ( still working or doing daily activities) see table 1
2) Stroke Risk Factors with 8 variables: Obesity, Diabetes, Blood Pressure, Atrial
Fibrillation, Smoker, Cholesterol, Angina Chest Pain and Transient Iscg ( ministrokes).
3) Count of actual Strokes of the subjects with three variables with values from [0
to 1] : First Stroke, Second Stroke and Third Stroke
4) Stroke target variables , the three that we want to predict: Surgery,
Rehabilitation and Length of Days Rehabilitation
We summarized these four groups of variables and their ranges on figure 1.
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Artificial intelligence analysis using Neural Network to predict three stroke parameters: Surgery
needed, Treatment, and Length of Stay for Rehabilitation by
Jorge Garza-Ulloa
Figure 1 Table 1 4 Groups variables for the proposed NN
To analyze this dataset we use WEKA Open Source tool for Data mining [3] these
information is summarized on the next four tables: Table 1, 2 and 3 shown the Input
variables and table 4 the Target Variables
Variable
AgeCat
(Subject Age)
Sub-Groups & Ranges
[45,55]=.25 ,
[56, 65]=.50 ,
[65,75]=.75 ,
[76,86]=1
Gender
Female=0,
Male=1
Active
No=0
Yes=1
Actual Data on Dataset
Table 1 Dataset Pre-analysis: Input Stroke Independent Variables
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Artificial intelligence analysis using Neural Network to predict three stroke parameters: Surgery
needed, Treatment, and Length of Stay for Rehabilitation by
Jorge Garza-Ulloa
Variable
Overweight
( based on normal
weight)
Diabetes
BloodPressure
Sub-Groups & Ranges
No=0,
Yes=1
Actual Data on Dataset
No=0,
Yes=1
Normal=0,
normal high=.5
high=1
Smoke
No=0,
Yes=1
(AF) Heart
palpitations
No=0,
Yes=1
Cholesterol
No=0,
Yes=1
Angina
(Chest Pain)
No=0,
Yes=1
Mini-Stroke
(TIA)
No=0,
Yes=1
Table 2 Dataset Pre-analysis: Input Stroke Risk Factor Variables
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Artificial intelligence analysis using Neural Network to predict three stroke parameters: Surgery
needed, Treatment, and Length of Stay for Rehabilitation by
Jorge Garza-Ulloa
Variable
Stroke1
Sub-Groups & Ranges
[Very Low, Low]=.25
[Low High, Medium Low]=.50
[Medium, Medium High]=.75 ,
[High, Very High]=1
Stroke2
[Very Low, Low]=.25
[Low High, Medium Low]=.50
[Medium, Medium High]=.75 ,
[High, Very High]=1
Stroke3
[Very Low, Low]=.25
[Low High, Medium Low]=.50
[Medium, Medium High]=.75 ,
[High, Very High]=1
Actual Data on Dataset
Table 3 Dataset Pre-analysis: Input Actual Stroke Factor Variables
Variable
Surgery
Sub-Groups & Ranges
[Very Low, Low]=.25
[Low High, Medium
Low]=.50 [Medium, Medium
High]=.75
[High, Very High]=1
Rehabilitation
[Very Low, Low]=.25
[Low High, Medium
Low]=.50 [Medium, Medium
High]=.75
[High, Very High]=1
Actual Data on Dataset
Length Of Stay [Very Low, Low]=.25
(LOS)
[Low High, Medium
rehabilitation Low]=.50 [Medium, Medium
High]=.75
[High, Very High]=1
Where 1=36 days of
Rehabilitation
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Artificial intelligence analysis using Neural Network to predict three stroke parameters: Surgery
needed, Treatment, and Length of Stay for Rehabilitation by
Jorge Garza-Ulloa
Table 4 Dataset Pre-analysis: Target Variables to predict
With dataset grouped on 4 logical groups our next step is to implement the Neural
Network for the minimum possible prediction error.
Implementation of the neural network:
We proposed to train a NN under Supervised mode (produce output in response to
sample inputs, making them particularly well-suited to modeling and controlling dynamic
systems, and predicting future events). Where we have 14 Input Variables on three
groups: Stroke independent, Risk Factors and Stroke Series and predict the Target
variables: Surgery, Rehab and Los_rehab. As shown on Figure 2.
Figure 2 Proposed Neural network to predict three stroke variables
The biggest problems on Neural Network are: How many Hidden Layer and
Neurons, and what Criteria? To predict the three parameters mentioned: Surgery,
Treatment and Days of rehabilitation using with the actual information available. We use
three different software available: NN Basic technique (with no automatic stepwise
functions, limited diagnostic), another NN semi-automatic technique (allows stepwise
function and good diagnostic) and one Neuro-Intelligence (use Genetic Algorithm to find
the best NN configuration and an excellent diagnostic tools) our proposed solution must
be minimum possible prediction error.
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Artificial intelligence analysis using Neural Network to predict three stroke parameters: Surgery
needed, Treatment, and Length of Stay for Rehabilitation by
Jorge Garza-Ulloa
1) NN Basic technique_ Analysis using WEKA Open Source tool for Data
mining [3].Weka includes a number of different techniques that can be useful
for forecast development. These include: Linear and logistic regression
Perceptron Models (Neural networks) and a variety of classification
algorithms are available as Standard algorithm like J48, which is version of
the last free version of Classification and regression trees (CART) is a nonparametric decision tree learning technique that produces either classification
or regression trees, depending on whether the dependent variable is
categorical or numeric. Unfortunately, the “work horse” linear regression
module in Weka is limited in usefulness: No automatic stepwise function,
Poor diagnostics Compare as others software. Weka Pro and Cons are shown
on Table 5. That why we didn’t choose Weka for our purpose on this research.
Weka techniques for NN
Different techniques as:
Linear and logistic
regression Perceptron
models (Neural
networks)
Weka classifiers for NN
Includes as: J-48 based
on Classification and
regression trees
Weka limitations for NN
On the Linear
Regression Module:
No automatic stepwise
function, Poor diagnostics
Table 5 Dataset Analysis using WEKA Tool to predict Stroke Variables
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Artificial intelligence analysis using Neural Network to predict three stroke parameters: Surgery
needed, Treatment, and Length of Stay for Rehabilitation by
Jorge Garza-Ulloa
2) NN semi-automatic technique that allows stepwise function and good
diagnostic. We choose Matlab Toolbox [2]. We based our topologies of NN
based on previous studies for Roy, Cheng, Chang, Moore De Luca[5]. The
choose to analyze four different topologies: Two with a Single hidden layer 22
and 33 neurons respectively and two NN with Two hidden layers 44 and 33
or 44 and 22 neurons respectively. Where each of the three targets was
assigned to one output neuron. This is an important characteristic of the
design resulting in orthogonal outputs that decrease the likelihood of
misclassification errors. They recommend a Tan-sigmoid transfer function
was used for the neurons of the hidden layers, and a linear transfer function
for the neurons of the output layer as shown on fig.3
Figure 3 Proposed Analysis of Four Neural networks to predict three stroke variables
To define the training function of the Neural network we have the option
to choose three transfer functions: Logsig (generate output between 0 and 1),
Tansig (multilayer networks) usually it is recommended for recognition
problems and Purelin (linear output neurons) recommend for fitting problems,
as shown on table 5.
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Artificial intelligence analysis using Neural Network to predict three stroke parameters: Surgery
needed, Treatment, and Length of Stay for Rehabilitation by
Jorge Garza-Ulloa
Table 5 Options available for transfer functions on NN
For training the proposed NN we have different algorithms as shown on
Fig. 4, each one with Adv and disadvantages for the kind of data that we need
to train. Roy, Cheng, Chang, Moore De Luca [5] recommend the use of Scaled
Conjugate Gradient SGM algorithm for solving the propose NN because SGM
deal with weights and biases of neurons it is a supervised learning algorithm
optimization class techniques well known in numerical analysis as the
Conjugate Gradient Methods, besides testing with different algorithms we
found the Bayesian regularization algorithm BR results very closed to the
results of the analysis of SGM.
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Artificial intelligence analysis using Neural Network to predict three stroke parameters: Surgery
needed, Treatment, and Length of Stay for Rehabilitation by
Jorge Garza-Ulloa
Figure 4 Algorithms available for train the proposed NN
For evaluate the results we use two criteria’s: Performance chart (should get a
decaying plot, since you are trying to minimize the error) and Regression
Parameters chart verifying the parameter R to be as high as possible. We form
two groups for testing one based on the using Scaled conjugate gradient
algorithm SCG results on table 6 and the other on Group B using BR
Bayesian Regularization table 7.
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Artificial intelligence analysis using Neural Network to predict three stroke parameters: Surgery
needed, Treatment, and Length of Stay for Rehabilitation by
Jorge Garza-Ulloa
Table 6 Evaluation NN results using SCG as training Algorithm
Table 7 Evaluation NN results using BR Bayesian Regularization
Analyzing the results we detect the optimal configuration was with the
Bayesian Regularization training method with a Performance curve showing
decaying plot and The Regression Value =.79 (21% forecasting error ), but
can we find a better solution for the proposed NN? To answer this question
we use another technique on the next step.
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Artificial intelligence analysis using Neural Network to predict three stroke parameters: Surgery
needed, Treatment, and Length of Stay for Rehabilitation by
Jorge Garza-Ulloa
3) Neuro-Intelligence that use Genetic Algorithm to find the best NN
configuration and an excellent diagnostic tools our proposed solution must be
minimum possible prediction error. We use Alyuda Neurointelligence
software because we have tool for Analysis, pre-processing, Architecture
search algorithm to recommend the best NN configuration.
Running the option on search NN best architecture the recommendation was a
network topology with 1 hidden layer with five neurons but for the inputs a
configuration of 30 connected to the 14 stroke variables and for output 7
connected to the 3 stroke target variables as shown on fig. 5.
Fig. 5 Recommend NN architecture for the proposed Stroke NN
Running the training on Neuro-Intelligence we validate the results that we
get using on NN semi-automatic technique using the Conjugate Gradient
Descent to a value of 78 (22 % forecasting error) ,other algorithm recommend
was the Batch Back Propagation and we get an improvement to
validation=.81 ( 19% forecasting error) as shown on table 8.
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Artificial intelligence analysis using Neural Network to predict three stroke parameters: Surgery
needed, Treatment, and Length of Stay for Rehabilitation by
Jorge Garza-Ulloa
NN
Topology
14 Input to
30-5-7
3 Outputs
Training
Algorithm
Batch Back
Propagation
Dataset Error s
14 Input to Conjugate
30-5-7
Gradient
3 Outputs Descent
Table 8 Validation of NN with topology 30-5-7 with two different algorithms
The NN recommended topology of 7 Inputs to 30-50-7 3 Outputs using Bach
Back Propagation for training and verifying the Output with the Data Target the
results on a chart are shown on Fig. 6
Fig. 6 Verifying Actual Target vs Output from the NN 30-50-7
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Artificial intelligence analysis using Neural Network to predict three stroke parameters: Surgery
needed, Treatment, and Length of Stay for Rehabilitation by
Jorge Garza-Ulloa
Experimental results:
Post-Validation of the neural network
Post-validate the proposed NN of 30-5-7 on two ways:
1) Post-Validation using new input data with a logical values and
expected logical values as a target. For example we expect that a healthy
subject from 45-55 years old, active, with no Stroke Risk Factors will
forecast a values of zero for: Surgery, Rehab and Los_Rehab, and a man
from 76-86 year old, active with all the Risk Factors and three strokes will
get a very medium value for Surgery, Medium High for Rehab and
Los_Rehab. These results are Shown on Table 9
2) Post-Validation using Random data generated with a uniform
distribution generate for the program itself. We verify the results
and they are shown on table 10.
Table 9 Post-Validation new input data with a logical Target values
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Artificial intelligence analysis using Neural Network to predict three stroke parameters: Surgery
needed, Treatment, and Length of Stay for Rehabilitation by
Jorge Garza-Ulloa
Table 10 Post-Validation new Random input values and forecast expected
The post-validation results where according with the values that we
expected. The show that the proposed NN of 30-5-7 it is working and
forecasting the expected values for: Surgery, rehab and Los_Rehab.
Conclusions:
Specific Conclusions: Based on the 14 Stroke input variables and the
3 output target Stroke values, this paper suggest that the forecasting of :
Surgery, Rehab and Los_Rehab it is possible using Neural network, a
bigger Dataset is recommended to achieve an optimized NN to have a
minimum of 10% of forecasting error.
General conclusions: ANN’s are a powerful technique utilized across
scientific disciplines. Theoretically well suited to non-linear processes like
this application, but NN it is not transparent to users, Hard to integrate
into forecast thinking with technically difficult to understand, raises risk of
misuse.
Further works:
Create a larger dataset with own lab reading values to have a better
control of the information and data to have realistic results and apply a
follow-up the subjects during its recovery with an the goal of feedback
and take appropriate forms to improve this recovery, and connect these
results with a complete Recovery systems for the Follow-up of the
subject and achieve the Database Acknowledgment (DBK) Based on:
Surgery, Treatment and Days of rehabilitation to take a decision based on
a System Design for Follow-Up of subjects with Mild Stroke to feedback
Medical Doctors if the subjects are responding to treatment for a faster
recovery.
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Artificial intelligence analysis using Neural Network to predict three stroke parameters: Surgery
needed, Treatment, and Length of Stay for Rehabilitation by
Jorge Garza-Ulloa
References:
[1] Stroke_full_database from Many Eyes An experiment brought to you by IBM
Research and the IBM Cognos software group on the link :
http://www-958.ibm.com/software/data/cognos/manyeyes/datasets/stroke-fulldataset/versions/1
[2] Neural Network Toolbox™ Mathworks (2009)
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http://www.cs.waikato.ac.nz/ml/weka/
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