Artificial intelligence analysis using Neural Network to predict three stroke parameters: Surgery needed, Treatment, and Length of Stay for Rehabilitation by Jorge Garza-Ulloa Page 1 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”.
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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
Page 1
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
Artificial intelligence analysis using Neural Network to predict three stroke parameters: Surgery
needed, Treatment, and Length of Stay for Rehabilitation by Jorge Garza-Ulloa
Page 9
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.
Artificial intelligence analysis using Neural Network to predict three stroke parameters: Surgery
needed, Treatment, and Length of Stay for Rehabilitation by Jorge Garza-Ulloa
Page 10
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.
Artificial intelligence analysis using Neural Network to predict three stroke parameters: Surgery
needed, Treatment, and Length of Stay for Rehabilitation by Jorge Garza-Ulloa
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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.
Artificial intelligence analysis using Neural Network to predict three stroke parameters: Surgery
needed, Treatment, and Length of Stay for Rehabilitation by Jorge Garza-Ulloa
Page 12
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.
Artificial intelligence analysis using Neural Network to predict three stroke parameters: Surgery
needed, Treatment, and Length of Stay for Rehabilitation by Jorge Garza-Ulloa
Page 13
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.
Artificial intelligence analysis using Neural Network to predict three stroke parameters: Surgery
needed, Treatment, and Length of Stay for Rehabilitation by Jorge Garza-Ulloa
Page 14
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
Artificial intelligence analysis using Neural Network to predict three stroke parameters: Surgery
needed, Treatment, and Length of Stay for Rehabilitation by Jorge Garza-Ulloa
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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
Artificial intelligence analysis using Neural Network to predict three stroke parameters: Surgery
needed, Treatment, and Length of Stay for Rehabilitation by Jorge Garza-Ulloa
Page 16
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.
Artificial intelligence analysis using Neural Network to predict three stroke parameters: Surgery
needed, Treatment, and Length of Stay for Rehabilitation by Jorge Garza-Ulloa
Page 17
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 :
[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.
[6] J. R. Lieberman, F. Dorey, and P. Shekelle, “Difference between patients’ and physicians’ evaluations of outcome after total hip arthroplasty,”
J. Bone Joint Surg., vol. 78, pp. 835–838, 1996. [4] D. B. Reuben, “What’s wrong with ADLs?,” J. Am. Geriatr. Soc., vol.43, pp. 936–937, 1995.
[7] K. Kiani, C. J. Snijders, and E. S. Gelsema, “Computerized analysis of daily life motor activity for ambulatory monitoring,” Technol. Health Care, vol. 5, pp. 307–
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,”
Ph.D. dissertation, Boston, MA, 2005.
[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.
Keith, C. V. Granger, B. B. Hamilton, and F. S. Sherwin, “The functional independence measure: A new tool for rehabilitation,” Adv. Clin. Rehabil.,
vol. 1, pp. 6–18, 1987. [13] J. Mao and A. K. Jain, “Artificial neural networks for feature extraction and
multivariate data projection,” IEEE Trans. Neural Networks, vol.6 Issue:2 1995