Home   >   CSC-OpenAccess Library   >    Manuscript Information
Full Text Available

(99.82KB)
This is an Open Access publication published under CSC-OpenAccess Policy.
Publications from CSC-OpenAccess Library are being accessed from over 74 countries worldwide.
Designing an Artificial Neural Network Model for the Prediction of Thrombo-embolic Stroke
Shanthi Dhanushkodi, G.Sahoo , Saravanan Nallaperumal
Pages - 10 - 18     |    Revised - 20-02-2009     |    Published - 15-03-2009
Volume - 3   Issue - 1    |    Publication Date - February 2009  Table of Contents
MORE INFORMATION
KEYWORDS
Artificial Intelligence, BPN, Neural Network, Thrombo-embolic Stroke
ABSTRACT
Artificial Neural Networks (ANN) are established analytical methods in bio-medical research. They have repeatedly outperformed traditional tools for pattern recognition and clinical outcome prediction while assuring continued adoption and learning. Extensive research had confirmed the utility of ANN for the solution of clinical diagnosis and prognostic problems. In this paper, we propose an ANN model that is understandable, practicable and capable of achieving accurate prediction of Thrombo-embolic Stroke disease. This model assists the physicians in taking decisions in the stages of diagnosis, based on the output generated by the system.
CITED BY (47)  
1 Radhimeenakshi, S., & Nasira, G. M. Prediction of Heart Disease using Neural Network with Back Propagation.
2 Rodriguez, R., Bila, J., Mexicano, A., Cervantes, S., Ponce, R., & Nghien, N. B. (2014, August). Hilbert-Huang transform and neural networks for electrocardiogram modeling and prediction. In Natural Computation (ICNC), 2014 10th International Conference on (pp. 561-567). IEEE.
3 Rohmana, I., & Arifudin, R. (2014). perbandingan jaringan syaraf tiruan dan naive bayes dalam deteksi seseorang terkena penyakit stroke. jurnal mipa, 37(1).
4 Gupta, N., & Mittal, A. (2014). Brain Ischemic Stroke Segmentation: A Survey. Journal of Multi Disciplinary Engineering Technologies Volume, 8(1), 1.
5 Golovko, VA, Dryvotynov, BV, Apanel, EN, Voitsekhovich, GY, & Mast?kyn, A. (2014). Neyroyntellektualnaya system diagnostics transient ischemic attacks. Artificial Intelligence, (2), 141-156.
6 Sankaranarayanan, S., & Perumal, T. P. (2014, March). Diabetic Prognosis through Data Mining Methods and Techniques. In Intelligent Computing Applications (ICICA), 2014 International Conference on (pp. 162-166). IEEE.
7 Bhatia, A., Mago, V., & Singh, R. (2014, September). Use of soft computing techniques in medical decision making: A survey. In Advances in Computing, Communications and Informatics (ICACCI, 2014 International Conference on (pp. 1131-1137). IEEE.
8 Golovko , VA , Drivotinov , BV , Apanel , EN , Voitsekhovitch , H. Yu , & Mastykin , A. ( 2014 ) . Neurointellectual system diagnosis of transient ischemic attacks. Artificial Intelligence.
9 Wahyunggoro, O., Permanasari, A. E., & Chamsudin, A. Utilization of Neural Network for Disease Forecasting.
10 Vaitsekhovich, H. Artificial Intelligence Methods in Diagnosis of Ischemic Stroke Diseases.
11 Loh, H. C. (2013, March). A Novel Approach to Predict Schizophrenia Disease: Conceptual Framework. In Applied Mechanics and Materials (Vol. 284, pp. 1596-1600).
12 Shannaq, B., & Thakkar, D. (2013). on the development of neural network models using data mining tools. asian journal of computer science & information technology, 2(7).
13 Gotti, F. J. A., Costa, I., & Shiguemori, E. H. (2013, October). inteligência computacional aplicada em avaliações da gestão acadêmica em uma ies. in proceedings of safety, health and environment world congress (vol. 11).
14 Barman, D., & Chowdhury, N. (2013). Estimation of Possible Profit/Loss of a New Movie Using “Natural Grouping” of Movie Genres. International Journal of Information Engineering and Electronic Business (IJIEEB), 5(4), 24.
15 Kumar, S., & Kumaravel, A. (2013). Diabetes Diagnosis using Artificial Neural Network. International Journal of Engineering Sciences & Research Technology, 1642-1644.
16 Khan, N., Gaurav, D., & Kandl, T. (2013). Performance Evaluation of Levenberg-Marquardt Technique in Error Reduction for Diabetes Condition Classification. Procedia Computer Science, 18, 2629-2637.
17 Waller, T., Nowak, R., Tkacz, M., Zapart, D., & Mazurek, U. (2013). Familial or Sporadic Idiopathic Scoliosis–classification based on artificial neural network and GAPDH and ACTB transcription profile. Biomedical engineering online, 12(1), 1.
18 Murthy, H. N., & Meenakshi, M. (2013). ANN model to predict coronary heart disease based on risk factors. Bonfring International Journal of Man Machine Interface, 3(2), 13-18.
19 Chitra, R., & Seenivasagam, V. (2013). Review of Heart Disease Prediction System Using Data Mining And Hybrid Intelligent Techniques. ISSN: 2229-6956 (ONLINE) ICTACT JOURNAL ON SOFT COMPUTING, 3(04).
20 DEVI, C. A., & Rajagopalan, S. P. (2013). brain stroke classification based on multi-layer perceptron using watershed segmentation and gabor filter. journal of theoretical & applied information technology, 56(3).
21 Köker, R. (2013). A genetic algorithm approach to a neural-network-based inverse kinematics solution of robotic manipulators based on error minimization. Information Sciences, 222, 528-543.
22 Golovko, V., Vaitsekhovich, H., Apanel, E., & Mastykin, A. (2012). Neural network model for transient ischemic attacks diagnostics. Optical Memory and Neural Networks, 21(3), 166-176.
23 Sudha, A., Gayathri, P., & Jaisankar, N. (2012). Effective Analysis and Predictive Model of Stroke Disease using Classification Methods. International Journal of Computer Applications, 43(14), 26-31.
24 Sudha, A., Gayathri, P., & Jaisankar, N. (2012). Utilization of Data mining Approaches for Prediction of Life Threatening Diseases Survivability. International Journal of Computer Applications (0975–8887) Volume.
25 Smitha, J. C., & Babu, S. S. (2012). A Broad review of noteworthy researches on brain abnormality detection with the aid of medical images. Eur. J. Sci. Res, 85(2), 279-304.
26 Hawamdeh, Z. M., Alshraideh, M. A., Al-Ajlouni, J. M., Salah, I. K., Holm, M. B., & Otom, A. H. (2012). Development of a decision support system to predict physicians’ rehabilitation protocols for patients with knee osteoarthritis. International Journal of Rehabilitation Research, 35(3), 214-219.
27 Mohanty, R. P., Sahoo, G., & Dasgupta, J. (2012). Identification of Risk Factors in Globally Outsourced Software Projects using Logistic Regression and ANN. International Journal of Supply Chain Management, 1(1).
28 Atanassova, P. A., Dimitrov, B. D., & Chalakova, N. T. (2012). Modelling of Transcranial Magnetic Stimulation in One-Year Follow-Up Study of Patients with Minor Ischaemic Stroke. INTECH Open Access Publisher.
29 Sabibullah, M. (2012, December). Prognostic neural network model for diabetic risks prediction. In Emerging Trends in Science, Engineering and Technology (INCOSET), 2012 International Conference on (pp. 392-395). IEEE.
30 Vaitsekhovich, H. Neural Networks in Disease Diagnostics. in baltic conference (p. 47).
31 Vaitsekhovich, H. Neural Networks in Ischemic Strokes Diagnostics.
32 Zhao, Y. Artificial Neural Network Modeling with Application to Nonlinear Dynamics.
33 R. Köker, “A Neuro-Genetic Approach to the Inverse Kinematics Solution of Robotic Manipulators” Scientific Research and Essays, 6(13), pp. 2784-2794, 4 July, 2011.
34 D. R. Chowdhury, M. Chatterjee and R. K. Samanta, “An Artificial Neural Network Model for Neonatal Disease Diagnosis” International Journal of Artificial Intelligence and Expert Systems (IJAE), 2 (3), pp. 96 – 106, Aug. 2011.
35 S. Gupta, S. Bhardwaj and P. K. Bhatia, “A Reminiscent Study of Nature Inspired Computation” International Journal of Advances in Engineering & Technology, 1(2), ,pp.117-125, May 2011.
36 M. A. Sapon, K. Ismail and S. Zainudin, “Prediction of Diabetes by Using Artificial Neural Networks" in Proceedings of International Conference on Circuits, System and Simulation IPCSIT vol.7 (2011) © (2011) IACSIT Press, Singapore, 2011, pp. 299-303.
37 P. Gómez-Gil, J. M. R. Cortes, S. E. P. Hernández and V. A. Aquino “A Neural Network Scheme for Long-Term Forecasting of Chaotic Time Series”, Neural Processing Letters, 33(3), pp. 215-233, 2011.
38 Golovko, VA, Voitsekhovich, GY, Apanel, EN, & Mast?kyn, A. (2011). Neural network for diagnosis transient ischemic attacks. Journal BrHTU (5) 71.
39 Das, D., & Kundu, M. (2011). Identification of Algal Biomass Production with Partial Least Squares & Neural Network. International Journal of Chemical Engineering and Applications, 2(4), 288.
40 Golovko, V., Apanel, E., Mastykin, A., & Vaitsekhovich, H. (2011). Neural networks in transient ischemic attacks diagnostics.
41 Gupta, S., Bhardwaj, S., & Bhatia, P. K. (2011). A reminiscent study of nature inspired computation. International Journal of Advances in Engineering & Technology, 1(2).
42 Hanifa, S. M. (2010). Prediction of Stroke Risk Through Stacked Topology of ANN Model. International Journal of Advanced Research in Computer Science, 1(4).
43 N. Islam,N. I. Bin Hamid , A. Mahmud , S..M. Rahman and A. H. Khan, ” Detection of Some Major Heart Diseases Using Fractal Analysis” International Journal of Biometrics and Bioinformatics (IJBB) Volume: 4 (2), pp. 63 – 70, June 2010.
44 A. Agarwal, A. S. Pandya and A. Arimoto, “A Novel Approach for Measuring Electrical Impedance Tomography for Local Tissue with Artificial Intelligent Algorithm” International Journal of Biometrics and Bioinformatics (IJBB) 3(5), pp. 66 – 81, Nov. 2009.
45 T. Rashid, “Classification of Churn and non-Churn Customers in Telecommunication Companies” International Journal of Biometrics and Bioinformatics (IJBB), 3(5), pp. 66-95, Nov. 2009.
46 Pandya, A. S., Arimoto, A., Agarwal, A., & Kinouchi, Y. (2009). A novel approach for measuring electrical impedance tomography for local tissue with artificial intelligent algorithm. International Journal of Biometrics and Bioinformatics, 3(5), 66.
47 Rashid, T. (2008). Classification of Churn and non-Churn customers for Telecommunication Companies. International Journal of Biometrics and Bioinformatics (IJBB), 3(5), 82-89.
1 Google Scholar 
2 ScientificCommons 
3 Academic Index 
4 CiteSeerX 
5 refSeek 
6 iSEEK 
7 Socol@r  
8 ResearchGATE 
9 Libsearch 
10 Bielefeld Academic Search Engine (BASE) 
11 Scribd 
12 WorldCat 
13 SlideShare 
14 PdfSR 
15 PDFCAST 
16 National Science Digital Library (NSDL) 
1 Mohr, J.P., (2001). Stroke Analysis, 4th Edition, Oxford Press, New York.
2 American Heart Association. Heart Disease and Stroke Statistics — 2004 Update. Dallas, Tex.: American Heart Association; 2003.
3 Maurizio Bevilacqua (2005), Failure rate prediction with artificial neural networks, Journal of Quality in Maintenance Engineering Vol. 11 No. 3, 2005 pp. 279-294 Emerald Group Publishing Limited 1355-2511
4 Nazeran, H., & Behbehani, K. (2001). Neural networks in processing and analysis of biomedical signals. In M. Akay (Ed.), Nonlinear biomedical signal processing: Fuzzy logic, neural networks and new algorithms, pp. 69–97.
5 Celler, B.G., & Chazal, P. (1998). Low computational cost classifiers for ECG diagnosis using neural networks. Proceedings of the International Conference of Engineering in Medicine & Biology Society (EMBC 1998) ,pp. 1337–1340.
6 Rupa banerjee et al.,(2003) , Predicting mortality in patients with cirrhosis of liver with application of neural network technology, 2003 Blackwell Publishing Asia Pvt. Ltd.
7 Green, M. et al.(2006).Comparison between neural networks and multiple logistic regression to predict acute coronary syndrome in the emergency room , Elsevier B.V.
8 Ham, P.M. and Han, S. "Classification of cardiac arrhythmias using fuzzy artmap," IEEE Transactions on Biomedical Engineering, 43(4): 425–430 (1996).
9 Modai, I., Israel, A., Mendel, S., Hines, E.L. and Weizman, R., "Neural network based on adaptive resonance theory as compared to experts in suggesting treatment for schizophrenic and unipolar depressed in-patients," Journal of Medical Systems, 20(6):403–412 (1996).
10 Mattias Ohlsson et al., (2006). The added value of ECG-gating for the diagnosis of myocardial infarction using myocardial perfusion scintigraphy and artificial neural networks, Clinical Physiological Functional Imaging 26, pp301–304.
11 Furness P N, Levesley J, Luo Z, Taub N, Kazi J I, Bates W D & Nicholson M L (1999) , A neural network approach to the biopsy diagnosis of early acute renal transplant rejection Histopathology 35, 461–467.
12 Papaloukas, C.,Fotiadis, D. I.,Likas, A., &Michalis, L. K. (2002b). An ischemia detection method based on artificial neural networks. Artificial Intelligence in Medicine, 24, 167–178
13 Elmer Andres Fernández, Rodolfo Valtuille, Jesus Rodriguez Presedo, and Peter Willshaw (2005) Comparison of Standard and Artificial Neural Network Estimators of Hemodialysis Adequacy , International Center for Artificial Organs and Transplantation. 29(2):159–165, Blackwell Publishing, Inc.
14 Peter Gjertsson et al.,(2006) The added value of ECG-gating for the diagnosis of myocardial infarction using myocardial perfusion scintigraphy and artificial neural networks, Clinical Physiological Functional Imaging 26, pp301–304.
15 Baxt WG. Application of artificial neural networks to clinical medicine. Lancet. 1995; 346 (8983): 1135- 8.
16 Rumelhart, D.E., McClelland, J.L., and the PDF Research Group (1986), Parallel Distributed Processing, MA: MIT Press, Cambridge.
17 Neuro Intelligence using Alyuda. Source Available at www.alyuda.com. Last Accessed 10 May 2008.
18 D.Shanthi, G.Sahoo & N.Saravanan (2008). Input Feature Selection using Hybrid Neuro-Genetic Approach in the diagnosis of Stroke. International Journal of Computer Science and Network Security, ISSN: 1738-7906, Vol.8, No.12, pp.99-107.
Mr. Shanthi Dhanushkodi
- India
dshan71@gmail.com
Dr. G.Sahoo
Birla Institute of Technology - India
Dr. Saravanan Nallaperumal
Birla Institute of Technology - India