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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

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, life-

supporting 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”.



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:



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 ( mini-

strokes).

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.



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 Sub-Groups & Ranges Actual Data on Dataset

AgeCat

(Subject Age)

[45,55]=.25 , [56, 65]=.50 , [65,75]=.75 , [76,86]=1

Gender

Female=0, Male=1

Active No=0

Yes=1

Table 1 Dataset Pre-analysis: Input Stroke Independent Variables




Overweight ( based on normal

weight)

No=0, Yes=1

Diabetes No=0,

Yes=1

BloodPressure 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




Stroke1

[Very Low, Low]=.25 [Low High, Medium Low]=.50 [Medium, Medium High]=.75 , [High, Very High]=1

Stroke2


Stroke3


Table 3 Dataset Pre-analysis: Input Actual Stroke Factor Variables


Surgery [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

Length Of Stay

(LOS) rehabilitation

[Very Low, Low]=.25 [Low High, Medium Low]=.50 [Medium, Medium High]=.75 [High, Very High]=1 Where 1=36 days of Rehabilitation



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.



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 non-

parametric 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 Weka classifiers for NN Weka limitations for NN

Different techniques as:

Linear and logistic

regression Perceptron

models (Neural

networks)

Includes as: J-48 based

on Classification and

regression trees

On the Linear

Regression Module:

No automatic stepwise

function, Poor diagnostics

Table 5 Dataset Analysis using WEKA Tool to predict Stroke Variables

http://en.wikipedia.org/wiki/Non-parametric_statistics

http://en.wikipedia.org/wiki/Non-parametric_statistics

http://en.wikipedia.org/wiki/Decision_tree_learning



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.



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.



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.



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.



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.



NN

Topology

Training

Algorithm

Dataset Error s

14 Input to

30-5-7

3 Outputs

Batch Back

Propagation

14 Input to

30-5-7

3 Outputs

Conjugate

Gradient

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



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



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.



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-full-dataset/versions/1

[2] Neural Network Toolbox™ Mathworks (2009)

[3] WEKA Open Sources tools for Data Mining; http://www.cs.waikato.ac.nz/ml/weka/

[4] NEURAL NETWORKS: Heikki N. Koivo (2008)

[5] Neural Network Biomedical Engineering IEEE TRANSACTIONS ON NEURAL SYSTEMS AND REHABILITATION ENGINEERING, VOL. 17, NO. 6, DECEMBER 2009

by Serge H. Roy, M. Samuel Cheng, Shey-Sheen Chang, John Moore, Gianluca De Luca,S. Hamid Nawab, Carlo J. De Luca.

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318, 1997.

[8] K. Kiani, C. J. Snijders, and E. S. Gelsema, “Recognition of daily life motor activity classes using an artificial neural network,” Arch. Phys. Med. Rehabil., vol. 79, pp. 147–154, 1998.

[9] ] D. M. Sherrill, P. Bonato, and C. J. De Luca, “A neural network approach to

monitor functional motor activities,” presented at the 2nd [10] M. S. Cheng, “Monitoring functional motor activities in patients with stroke,”

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[11] A. R. Fugl-Meyer, L. Jaasko, I. Leyman, S. Olsson, and S. Steglind, “The post-stroke hemiplegic patient. I. A method for evaluation of physical performance,” Scand J. Rehabil. Med., vol. 7, pp. 13–31, 1975. [12] R. A.

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