Different Approaches of Soft Computing Techniques (Inference System) which are used in Clinical Decision Support System for Risk based Prioritization


  • Faiyaz Ahmad Assistant Professor Integral University, Lucknow
  • Manuj Darbari
  • Rishi Asthana


CDSS, Information System, Health care Environment, Soft Computing


This paper briefly introduces soft computing techniques and present miscellaneous application in clinical decision support system  domine. study detects which methodology or methodologies of soft computing are frequently used together to solve the specific problems of  risk based prioritization for decease severity.  With the fulfillment of these work makes several major contribution of the current knowledge of mechanism of different intelligent system such as Fuzzy Logic, ANN and Artificial Neuro Fuzzy for correct diagnosis .


Hayward G, Davidson V.(2003) Fuzzy logic applications.Analyst;128:1304-6.

Zadeh L. A. (1973). ―Outline of a new approach to the analysis of complex systems and decision processes‖. IEEE Transactionson Systems, Man and Cybernetics.3: 28-44.

Munakata T, Jani Y.(1994) Fuzzy systems: an overview. Communications of the ACM; 37(3):69-76.

Fathi-Torbaghan M, Meyer D. MEDUSA:A fuzzy expert system for medical diagnosis of acute abdominal pain. Meth. InfJ Med, 1994; 33(5):522-529.

Shiomi S, Kuroki T, Jomura H, et al.(1995) Diagnosis of chronic liver disease from liver scintiscans by fuzzy reasoning. J. Nucl. Med.; 36(4): 593-598.

Payne T.(2000) Computer decision support system. Chest; 118:47-52.

Silverman EK, Kwiatkowski DJ, Sylvia JS, Lazarus R, Drazen JM, Lange C, Laird NM, Weiss ST. ( 2003) Family-based association analysis of beta2-adrenergic receptor polymorphisms in the Childhood Asthma Management Program. J Allergy Clin Immunol;112(5):870–6.

Nair SJ, Daigle KL, DeCuir P, Lapin CD, Schramm CM.( 2005), The influence of pulmonary function testing on the management of Asthma in children. J Pediatr; 147(6):797–801.

Zadeh L. A. (1996). ―Fuzzy Sets, Fuzzy Logic, Fuzzy Systems‖. World Scientific Press. 57-68.

Rajasekaran S. and Vijayalakshmi, P. (1997). ―Neural Network Fuzzy Logic and Genetic Algorithm‖. Veena Publication. 65-115.

Bourbakis NG.(2003), Bio-imaging and bio-informatics IEEE Transactions on Systems, Man and Cybernetics, Part B: Cybernetics;33(5): 726–727.

Zaus, M. (1999) Crisp and Soft Computing with Hypercubical Calculus. Physica, Heidelberg.

Avery G.; Glass,P.(1988) Retinopathy of prematurity: what causes it?Clin Perinatol,15,917-28.

Stewart AW, Mitchell EA, Pearce N,(2001) symptoms of Asthma and other atopic diseases in children (ISSAC). Int J Epidemiology; 30(1):173-9.

Mahapatra P.(1993) Social, economic and cultural aspects of Asthma: an exploratory study in Andra Pradesh, India. Hyderabad, India: Institute of Health Systems.

Kemp JP, Berkowitz RB, Miller SD, Murray JJ, Nolop K, Harrison JE.(2000) Mometasone furoate administered once daily is as effective as twice-daily administration for treatment of mild-to-moderate persistent Asthma. J Allergy Clin Immunol; 106(3):485–92.

Tattersfield AE, Knox AJ, Britton JR, Hall IP.(2002) Asthma. Lancet; 360(9342):1313-22.

Neffen H, Fritscher C, Schacht FC, Levy G, Chiarella P, Soriano JB, et al.( 2005) Asthma control in Latin America: the Asthma Insights and Reality in Latin America (AIRLA) survey. Rev Panam Salud Publica; 17(3):191-7.

Torres,A., Nieto,J.J. (2003) The fuzzy polynucleotide space :basic properties.Bioinformatics;19(5):587-592.

Peters RM, Shanies SA, Peters JC.(1995) Fuzzy cluster analysis of positive stress tests, a new method of combining exercise test variables to predict extent of coronary artery disease. Am. J. Cardiol; 76(10): 648-651.

Slader CA, Reddel HK, Spencer LM, Belousova EG, Arm our CL, Bosnic-Anticevich SZ, Thien FC, Jenkins CR.(2006) Double blind randomised controlled trial of two different breathing techniques in the management of Asthma. Thorax; 61(8):651–6. Epub March.

EPR-2, Expert panel report 2: guidelines for the diagnosis and management of Asthma (1997). NIH Publication No. 97-4051.

Stout JW, Visness CM, Enright P, Lamm C, Shapiro G, Gan VN, Adams GK III, Mitchell HE.(2006) Classification of Asthma severity in children: the contribution of pulmonary function testing.Arch Pediatr Adolesc Med;160(8):844.

Holgate ST, Chuchalin AG, Hebert J, Lotvall J, Persson GB, Chung KF, Bousquet J,Kerstjens HA, Fox H, Thirlwell J, et al. (2004); Omalizumab 011 International Study Group. Efficacy and safety of a recombinant anti-immunoglobulin E antibody (omalizumab) in severe allergic Asthma. Clin Exp Allergy;34(4):632–8.

EPR-Update (2002), Expert panel report: guidelines for the diagnosis and management of Asthma. Update on selected topics 2002 (EPR-Update 2002). NIH Publication No. 02-5074. Bethesda, MD, U.S. Department of Health and Human Services; National Institutes of Health; National Heart, Lung, and Blood Institute; National Asthma Education and Prevention Program.

Beuther DA, Weiss ST, Sutherland ER.(2006) Obesity and Asthma. Am J Respir Crit Care Med; 174(2):112-9.

Sabina AB, Williams AL, Wall HK, Bansal S, Chupp G, Katz DL. (2005) Yoga intervention for adults with mild-to-moderate Asthma: a pilot study. Ann Allergy Asthma Immunol; 94(5):543–8.

Shore SA, Fredberg JJ.(2005) Obesity, smooth muscle, and airway hyper responsiveness. J Allergy Clin Immunol; 115(5):925-7.

Bacharier LB, Strunk RC, Mauger D, White D, and Lemanske RF Jr, Sorkness CA.(2004) Classifying Asthma severity in children: mismatch between symptoms, medication use, and lung function. Am J Respir Crit Care Med; 170(4):426–32.

Eady RP.( 1986) The pharmacology of nedocromil sodium. Eur J Respir Dis Suppl; 147:112–119. Review.

Lehrer PM, Vaschillo E, VaschilloS B, Lu SE, Scardella A, Siddique M, Habib RH.(2004) Biofeedback treatment for Asthma. Chest; 126(2):352–61.

Lane SJ, Arm JP, Staynov DZ, Lee TH.(1994) Chemical mutational analysis of the human glucocorticoid receptor cDNA in glucocorticoid-resistant bronchial Asthma. Am J Respir Cell Mol Biol; 11(1):42-8.

Binaghi E, De Giorgi 0, Maggi G, Motta T, Rampini A.(1993) Computer-assisted diagnosis of postmenopausal osteoporosis using a fuzzy expert system shell. Comput. Biomed.Res; 26: 498-516.

Nieto,J.J. and Torres,A.(2002) Midpoints for fuzzy sets and their application in medicine. Artificial Intelligence in Medicine.

Ying H, McEachem M, Eddleman DW, Sheppard LC.(1992) Fuzzy control of mean arterial pressure in postsurgical patients with sodium nitroprusside infusion IEEE Trans Biomed. Eng; 39(10): 1060-1070.

Kosko B.(1992) Neural Networks and Fuzzy Systems. Englewood Cliffs, NJ: Prentice-Hall.

A. Azadeh, S.F. Ghaderi, S. Tarverdian, M. Saberi (2007), Integration of artificialneuralnetworks and genetic algorithm to predict electrical energy consumption, Applied Mathematics and Computation, Volume 186, Issue 2, Pages 1731–1741.

Hikmet Esen, Filiz Ozgen, Mehmet Esen, Abdulkadir Sengur (2009), Artificial neural network and wavelet neural network approaches for modelling of a solar air heater, Expert Systems with Applications, Volume 36, Issue 8, Pages 11240–11248.

Dr. S. Santhosh Baboo and I.Kadar Shereef (2010), An Efficient Weather Forecasting System using Artificial Neural Network, International Journal of Environmental Science and Development, Vol. 1, No. 4, pp-321-326.

Sumit Goyal, Gyanendra Kumar Goyal (2012), Estimating Processed Cheese Shelf Life with Artificial Neural Networks, IAES International Journal of Artificial Intelligence (IJ-AI), Vol. 1, No. 1, March 2012, pp. 19 - 24.

Kasabov, N.K. (2002), DENFIS: dynamic evolving neural-fuzzy inference system and its application for time-series prediction, Fuzzy Systems, Volume: 10, Issue: 2, Page(s): 144- 154.

Attila Licsár, Tamás Szirányi (2005), User-adaptive hand gesture recognition system with interactive training, Image and Vision Computing, Volume 23, Issue 12, Pages 1102–1114.

Dipali M. Joshi, Dr.N. K. Rana, V. M. Misra (2010), Classification of Brain Cancer Using Artificial Neural Network, Electronic Computer Technology (ICECT), 2010 International Conference on, Page(s): 112 - 116.

Satish K. Shah, Ms.Dharmistha D. Vishwakarma (2010), Development and Simulation of Artificial Neural Network based decision on parametric values 98 for Performance Optimization of Reactive Routing Protocol for MANET using Qualnet, 2010 International Conference on Computational Intelligence and Communication Networks, Page(s): 167- 171.

Jayant D. Sawarkar, Umesh L. Kulkarni, Dr. Sudhir kumar Sawarkar (), Prediction of Short Term Electric Load Using Artificial Neural Network, International Journal of Electronics and Computer Science Engineering, pp-992-999.

Collins W., Tissot P.(2008). Use of an artificial neural network to forecast thunderstorm location, Proceedings of the Fifth Conference on Artificial Intelligence Applications to Environmental Science, Published in Journal of AMS., San Antonio, TX, January, 2008.

Sumit Goyal, Gyanendra Kumar Goyal (2012), Predicting Shelf Life of Burfi through soft Computing, I.J. Information Engineering and Electronic Business,3, 26-33.

Adeoye, Adeyinka O. Moses (2009) Customer-led conceptual design system. PhD thesis, Dublin City University.

David Mendes, José A. Marengo (2010), Temporal downscaling: a comparison between artificial neural network and autocorrelation techniques over the Amazon Basin in present and future climate change scenarios, Theoretical and Applied Climatology, Volume 100, Issue 3-4, pp 413-421.

Ruchika Malhotra, Ankita Jain (2011), Software Effort Prediction using Statistical and Machine Learning Methods, (IJACSA) International Journal of Advanced Computer Science and Applications, Vol. 2, No.1,Page- 145-152.

A.D.Dongare, R.R.Kharde, Amit D.Kachare (2012), Introduction to Artificial Neural Network, International Journal of Engineering and Innovative Technology (IJEIT), Volume 2, Issue 1, page 189-193.

Philippe Ackerer, Maria Laura Foddis, Augusto Montisci, Gabriele Uras (2010), Identification of the unknown pollution source in the Alsatian aquifer (France) through groundwater modelling and Artificial Neural Networks applications, World Wide Workshop for Young Environmental Scientists: 2010, Arcueil : France (2010).

P. Konar , P. Chattopadhyay (2011), Bearing fault detection of induction motor using wavelet and Support Vector Machines (SVMs), Applied Soft Computing, Volume 11, Issue 6, Pages 4203–4211. Journal of Engineering Science and Technology Review, Special Issue on Econophysics, 4 (3); 277 – 280.

S. Savvidis, D. Ginoglou (2011), Environmental Indexes and Financial Ratios, Journal of Engineering Science and Technology Review, Special Issue on Econophysics, 4 (3); 277 – 280.

Israel Gonzalez-Carrasco, Angel Garcia-Crespo, Belen Ruiz-Mezcua, Jose Luis Lopez-Cuadrado (2012), An optimization methodology for machine learning strategies and regression problems in ballistic impact scenarios, Applied Intelligence, Volume 36, Issue 2, pp 424-441.

Tomislav Rolich, Anica Hursa Šajatović, Daniela Zavec Pavlinić (2010), Application of artificial neural network (ANN) for prediction of fabrics‘ extensibility, Fibers and Polymers, Volume 11, Issue 6, pp 917-923.

V.Ramesh, P.Parkavi, P.Yasodha (2011), Performance Analysis of Data Mining Techniques for Placement Chance Prediction, International Journal of Scientific & Engineering Research Volume 2, Issue 8, pp- 1-6.

Christof Koch, Idan Segev (200), the role of single neurons in information processing, Nature America Inc. • http://neurosci.nature.com, pp-1171-1177.

Rhona McGonigal, Edward G. Rowan, Kay N. Greenshields, Susan K. Halstead, Peter D. Humphreys, Russell P. Rother, Koichi Furukawa and Hugh J. Willison (2010), Anti-GD1a antibodies activate complement and calpain to injure distal motor nodes of Ranvier in mice, Oxford JournalsMedicine Brain Volume 133, Issue 7Pp. 1944-1960.

Dr. Vladimir G. Ivancevic, Dr. Tijana T. Ivancevic (2010), Brain and Classical Neural Networks, Quantum Neural Computation Intelligent Systems, Control and Automation: Science and Engineering Volume 40, 2010, pp 43-150.

VG Ivancevic, TT Ivancevic (2007), Neuro-fuzzy associative machinery for comprehensive brain and cognition modelling, pp-1-15.

Joachim Lübke, Dirk Feldmeyer (2007), excitatory signal flow and connectivity in a cortical column: focus on barrel cortex, Brain Structure and Function, Volume 212, Issue 1, pp 3-17.

Dinesh Kumar Gupta (2010), Modeling the Relationship between Air Quality and Intelligent Transportation, pp- 86-99.

Bertil Hanström (2004), some points on the phylogeny of nerve cells and of the central nervous system of invertebrates, Volume 46, Issue 2, pages 475– 493.

Isabelle Dusart*, Constantino Sotelo (2004), Lack of Purkinje cell loss in adult rat cerebellum following protracted axotomy: Degenerative changes and

David R. Ladle, Eline Pecho-Vrieseling, Silvia Arber (2007), Assembly of Motor Circuits in the Spinal Cord: Driven to Function by Genetic and Experience-Dependent Mechanisms, Nueron, olume 56, Issue 2, Pages- 270– 283.

M S Alexander, F Biering-Sorensen, D Bodner, N L Brackett, D Cardenas, S Charlifue, G Creasey, V Dietz, J Ditunno, W Donovan11, S L Elliott, I Estores, D E Graves, B Green, A Gousse, A B Jackson, M Kennelly, A-K Karlsson, A Krassioukov, K Krogh, T Linsenmeyer, R Marino, C J Mathias, I Perkash, A W Sheel, G Shilero, B Schurch, J Sonksen, S Stiens, J Wecht, L A Wuermser and J-J Wyndaele (2009), International standards to document remaining autonomic function after spinal cord injury, Spinal Cord, 47, 36– 43.

Henry Markram, Maria Toledo-Rodriguez, Yun Wang, Anirudh Gupta, Gilad Silberberg & Caizhi Wu (2004), Interneurons of the neocortical inhibitory system, Nature Reviews Neuroscience 5, 793-807.

Saroj Kumar Gupta, M. V. Jagannatha Reddy and Dr. A. Nanda Kumar (2010), Possibilistic Clustering Adaptive Smoothing Bilateral Filter Using Artificial Neural Network, IACSIT International Journal of Engineering and Technology, Vol.2, No.6, pp-499-503.

Sreekala, P., Kanjirapally, Jose, V. (2012), Application of neural network in speed control of brushless DC motor using soft switching inverter, Innovative Practices and Future Trends (AICERA), 2012 IEEE International Conference, Page(s): 1- 5.

Pritha, D.N., Savitha, L. ; Shylaja, S.S. (2010), Face Recognition by Feedforward Neural Network Using Laplacian of Gaussian Filter and Singular Value Decomposition, Integrated Intelligent Computing (ICIIC), 2010 First International Conference, Page(s): 56- 61.

Adesesan B. Adeyemo and Oluwafemi Oriola (2010), Personnel Audit Using a Forensic Mining Technique, IJCSI International Journal of Computer Science Issues, Vol. 7, Issue 6,pp- 222-231.

I Samy,J Whidborne, I Postlethwaite (2011), A comparison of neural networks for FDI of rolling element bearings – demonstrated on experimental rig data, Journal of Aerospace Engineering., vol. 225 no. 9 1012-1026.

Xuejun Liao, Ya Xue, Lawrence Carin (2005), Logistic regression with an auxiliary data source, ICML '05 Proceedings of the 22nd international conference on Machine learning Pages 505 - 512.

Z Zhang, , K Friedrich (2003),Artificial neural networks applied to polymer composites: a review, Composites Science and Technology, Volume 63, Issue 14,Pages 2029–2044.

Dimitris C. Psichogios, Lyle H. Ungar (2003), A hybrid neural network-first principles approach to process modeling, AIChE Journal, Volume 38, Issue 10, pages 1499–1511.

Manuel Leobardo Zavala-Arriaza, Fevrier Valdez, Patricia Melin (2012), Architecture of Modular Neural Network in Pattern Recognition, Recent Advances on Hybrid Intelligent Systems Studies in Computational Intelligence Volume 451, 2013, pp 211-219.

P Srinivasulu, D Nagaraju, P Ramesh Kumar, and K Nageswara Rao (2009), Classifying the Network Intrusion Attacks using Data Mining Classification Methods and their Performance Comparison, IJCSNS International Journal of Computer Science and Network Security, VOL.9 No.6,pp-11-18.

Paul Viola, Michael J. Jones (2004), Robust Real-Time Face Detection, International Journal of Computer Vision, Volume 57, Issue 2, pp 137-154.

Budiman Putra, Bagus Tris Atmaja, Syahroni Hidayat (2012), Fusion of artificial neural network and fuzzy system for short term weather forecasting, Volume 4, Number 2, Pages 210-226.

F Amegashie, J Q Shang, E K Yanful, W Ding, S Al-Martini (2006), Using complex permittivity and artificial neural networks to identify and classify copper, zinc, and lead contamination in soil, Canadian Geotechnical Journal, 43(1): 100-109.

L Pandola, C Cattadori, c, N Ferrari (2004), Neuralnetwork pulse shape analysis for proportional counters events, Nuclear Instruments and Methods in Physics Research Section A: Accelerators, Spectrometers, Detectors and Associated Equipment, Volume 522, Issue 3, Pages 521–528.

Parvin, H., Alizadeh, H. ; Minaei-Bidgoli, B. ; Analoui, M., (2008), A Scalable Method for Improving the Performance of Classifiers in Multiclass Applications by Pairwise Classifiers and GA, Volume: 2, Page(s): 137- 142 .

M. Egmont-Petersen, D. de Ridder, H. Handels, (2002), Image processing with neural networks—a review, Pattern Recognition, Volume 35, Issue 10,Pages 2279–2301.

Hamid Parvin, Hosein Alizadeh and Behrouz Minaei-Bidgoli, (2012), A New Divide and Conquer Based Classification for OCR, "Convergence and Hybrid Information Technologies, pp-1-11.

Juan de Lara, Hans Vangheluwe, (2002), AToM3: A Tool for Multi-formalism and Meta-modelling, Fundamental Approaches to Software Engineering, Lecture Notes in Computer Science Volume 2306,pp 174-188.

C. W. Dawson, R. L. Wilby, (2005), Hydrological modelling using artificial neural networks, Progress in Physical Geography, vol. 25 no. 1 80-108.

Mantas LukoÅ¡eviÄius, Herbert Jaeger, (2009), Reservoir computing approaches to recurrentneuralnetwork training, Computer Science Review, Volume 3, Issue 3,Pages 127–149.

K. Tsagkaris, A. Katidiotis, P. Demestichas, (2008), Neuralnetwork-based learning schemes for cognitive radio systems, Computer Communications, Volume 31, Issue 14,Pages 3394–3404.

Tarun Kumar, Kushal Veer Singh and Shekhar Malik, (2011), Artificial Neural Network in Face Detection. International Journal of Computer Applications 14(3):5–7.

Ajith Abraham (2005), Artificial Neural Networks, Handbook of Measuring System Design, pp-22-92.

Ue-Pyng Wen, Kuen-Ming Lan, Hsu-Shih Shih, (2009), A review of Hopfield neuralnetworks for solving mathematical programming problems, European Journal of Operational Research, Volume 198, Issue 3,Pages 675–687.

Yashpal Singh, Alok Singh Chauhan, (2009), Neural Networks In Data Mining, Journal of Theoretical and Applied Information Technology, pp-37-42.

Alok Madan, (2005), Vibration control of building structures using self-organizing and self-learning neural networks, Journal of Sound and Vibration, Volume 287, Issues 4–5, Pages 759–784.

Taiwo Ayodele, Shikun Zhou, Rinat Khusainov, (2010), Email Classification Using Back Propagation Technique, International Journal of Intelligent Computing Research (IJICR), Volume 1, Issue 1/2, pp-3-9.

Aleem Ali, (2012), A CONCISE ARTIFICIAL NEURAL NETWORK IN DATA MINING, International Journal of Research in Engineering & Applied Sciences, Volume 2, Issue 2, pp-418-428.

P. Bunnoon, K. Chalermyanont, and C. Limsakul, (2010), The Comparison of Mid Term Load Forecasting between Multi-Regional and Whole Country Area Using Artificial Neural Network, International Journal of Computer and Electrical Engineering, Vol. 2, No. 2, April,pp-1793-8163.

PETER STONE, v, (2000), Multiagent Systems: A Survey from a Machine Learning Perspective, Autonomous Robots, 8, 345–383.

Elmer A. Fernández, Rodolfo Valtuille, Mónica Balzarini, (2013), Artificial Neural Networks Applications in Dialysis, Modeling and Control of Dialysis Systems, Studies in Computational Intelligence Volume 405,pp 1145-1179.

Sushmita Mitra, Sankar K. Pal, (2005), Fuzzy sets in pattern recognition and machine intelligence, Fuzzy Sets and Systems, Volume 156, Issue 3,Pages 381–386.

Manish Kakar, Håkan Nyström, Lasse Rye Aarup, Trine Jakobi Nøttrup,Dag Rune Olsen, (2005), Respiratory motion prediction by using the adaptive neuro fuzzy inference system (ANFIS), Physics in Medicine and Biology, Volume 50, Number 19, pp-4721.

Albertos, P. , Sala, A. , (2004), Perspectives of fuzzy control: lights and shadows, Intelligent Systems, 2004. Proceedings. 2004 2nd International IEEE Conference, Volume: 1, Page(s): 25- 32.

Toha, S.F. , Tokhi, M.O. , (2010), ANFIS modelling of a twin rotor system using particle swarm optimisation and RLS, Cybernetic Intelligent Systems (CIS), 2010 IEEE 9th International Conference, Page(s): 1- 6.

Kun-Chieh Wang , (2006), Thermal Error Modeling of a Machining Center using Grey System Theory and Adaptive Network-Based Fuzzy Inference System, Cybernetics and Intelligent Systems, 2006 IEEE Conference, Page(s): 1- 6.

Ali, W. , Shamsuddin, S.M. , (2009), Computer Systems and Applications, 2009. AICCSA 2009. IEEE/ACS International Conference, Page(s): 888- 895.

S. Ravi,M. Sudha, P. A. Balakrishnan, (2011), Design of Intelligent Self-Tuning GA ANFIS Temperature Controller for Plastic Extrusion System, Modelling and Simulation in Engineering , Article No. 12, pp-1-8.

S. Nefti, M. Oussalah, K. Djouani, J. Pontnau, (2001), Intelligent Adaptive Mobile Robot Navigation, Journal of Intelligent and Robotic Systems, Volume 30, Issue 4, pp 311-329.

Vieira, D.A.G. , Caminhas, W.M. ; Vasconcelos, J.A., (2004), Extracting sensitivity information of electromagnetic device models using a modified ANFIS topology, Magnetics, Volume: 40 , Issue: 2, Page(s): 1180- 1183.

Ramesh, M. , Laxmi, A.J. , (2012), Fault identification in HVDC using artificial intelligence — Recent trends and perspective,Power, Signals, Controls and Computation (EPSCICON), 2012 International Conference, Page(s): 1- 6.

Elif Derya Übeyli, (2009), Combined neural networks for diagnosis of erythemato-squamous diseases, Expert Systems with Applications, Volume 36, Issue 3, Part 1, Pages 5107–5112.

Karim Salahshoor, Mojtaba Kordestani, Majid S. Khoshro, (2010), Fault detection and diagnosis of an industrial steam turbine using fusion of SVM (support vector machine) and ANFIS (adaptive neuro-fuzzy inference system) classifiers, Energy, Volume 35, Issue 12, Pages 5472–5482.

F.E. Ciarapica, G. Giacchetta, (2006), Managing the condition-based maintenance of a combined-cycle power plant: An approach using soft computing techniques, Journal of Loss Prevention in the Process Industries, Volume 19, Issue 4,Pages 316–325.

J.-S. R. Jang, (1993), ANFIS: Adaptive-Network-based Fuzzy Inference Systems. IEEE Transaction on Systems, Man and Cybernetice, pages 665-685.

Jettrey T. Drake and Nadipuram R. Prasad, (2001), ANFIS for parameter estimation in coherent communications phase synchronization. IEEE Signal Processing Society Workshop, page 433-442.

Chia-Chi Chen Wen-Liang and Shing-Chia Chen, (2001),ANFIS based PRML system for read-out RF signal. IFSA World congress and 20th NAFIPS International conference, page 912-917.

K. Polat, S.Gunes,(2007), ―Automatic determination of diseases related to lymph system from lymphography data using principles component analysis (PCA), fuzzy weighting pre-processing and ANFIS,‖ Expert Systems with

Applications: An International Journal, vol. 33 , Pages: 636-641.

Shih-Wei Chen, Sheng-Huang Lin, Lun-De Liao, Hsin-Yi Lai, Yu-Cheng Pei, Te-Son Kuo, Chin-Teng Lin,Jyh-Yeong Chang, You-Yin Chen, Yu-Chun Lo, Shin-Yuan Chen, Robby Wu, Siny Tsang, (2011), Quantification and recognition of parkinsonian gait from monocular video imaging using kernelbased principal component analysis, BioMedical Engineering OnLine 2011, 10:99, pp-2-21.

Cihat Ozhasoglu, Martin J Murphy,(2002), Issues in respiratorymotion compensation during external-beam radiotherapy, International Journal of Radiation Oncology*Biology*Physics, Volume 52, Issue 5, Pages 1389–1399.

Martina Mueller, Carol L Wagner, David J Annibale, Rebecca G Knapp, Thomas C Hulsey, Jonas S Almeida, (2003), Parameter selection for and implementation of a web-based decision-support tool to predict extubation outcome in premature infants, BMC Medical Informatics and Decision Making, Volume 6, pp-1-13.

Chin-Teng Lin , Chun-Lung Chang ; Wen-Chang Cheng , (2004), A recurrent fuzzy cellular neural network system with automatic structure and template learning, Circuits and Systems I, Volume: 51 , Issue: 5, Page(s): 1024- 1035.

M.M. Gupta, D.H. Rao (1994), On the principles of fuzzy neural networks, Fuzzy Sets and Systems, Volume 61, Issue 1, Pages 1–18.

Adachi, G. , Horikawa, S.-I. ; Furuhashi, T. ; Uchikawa, K., (1995), A new linguistic design method of fuzzy controller using a description of dynamical behavior of fuzzy control systems, American Control Conference, Volume: 3, Page(s): 2282- 2286.

Shin-ichi Horikawa, Takeshi Furuhashi, Yoshiki Uchikawa, (1995), A new type of fuzzy neural network based on a truth space approach for automatic acquisition of fuzzyrules with linguistic hedges, International Journal of Approximate Reasoning, Volume 13, Issue 4, Pages 249–268.

Takashi Hasegawa, Shin-ichi Horikawa, Takeshi Furuhashi, Yoshiki Uchikawa, (1995), On design of adaptivefuzzycontroller using fuzzy neural networks and a description of its dynamical behavior, Fuzzy Sets and Systems, Volume 71, Issue 1,Pages 3–23.

Luigi Benecchi, (2006), Neuro-fuzzy system for prostatecancer diagnosis, Urology, Volume 68, Issue 2,Pages 357–361.

Elif Derya Übeyli, (2009), Combined neural networks for diagnosis of erythemato-squamous diseases, Expert Systems with Applications, Volume 36, Issue 3, Part 1,Pages 5107–5112.

Iwasokun G. B., Akinyokun O. C., Alese B. K. & Olabode O, (2011), Adaptive and Faster Approach to Fingerprint Minutiae Extraction and Validation, International Journal of Computer Science and Security (IJCSS), Volume (5) : Issue (4), pp-414-424.

Iwasokun Gabriel Babatunde, Akinyokun Oluwole Charles, Alese Boniface Kayode, Olabode Olatunbosun Olatunbosun, (2012), A Multi-Level Model for Fingerprint Image Enhancement, vol. 4 no. 1, pp-155-174.

Ali Jalalia, Ali Ghaffarib, Parham Ghorbaniana, Chandrasekhar Nataraj, (2011), Identification of sympathetic and parasympathetic nerves function in cardiovascularregulation using ANFIS approximation, Artificial Intelligence in Medicine, Volume 52, Issue 1,Pages 27–32.

C. Nataraj, A. Jalali and P. Ghorbanian, (2011), Application of Computational Intelligence Techniques for Cardiovascular Diagnostics, The Cardiovascular System – Physiology, Diagnostics and Clinical Implications, pp-211-240.

185. Merrikh-Bayat, F., Bagheri Shouraki, S. , (2011), Memristive Neuro-Fuzzy System, Systems, Man, and Cybernetics, Part B: Cybernetics,Volume:

Babazadeh Khameneh N, Arabalibeik H, Salehian P, Setayeshi S, (2012), Abnormal red blood cells detection using adaptive neuro-fuzzy system, Studies in Health Technology and Informatics, vol.-173, pp: 30-34.

Shahnaz Khaleghipour, Mohsen Masjedi, Hassan Ahade, Meersalahodin Enayate, Gholamreza Pasha, Farah Nadery, Gholamhossein Ahmadzade, (2012), Morning and nocturnal serum melatonin rhythm levels in patients with major depressive disorder: an analytical cross-sectional study, Sao Paulo Med J. 2012; 130(3):167-72.

Feng Liu, Chai Quek,Geok See Ng, (2007), A Novel Generic Hebbian Ordering-Based Fuzzy Rule Base Reduction Approach to Mamdani Neuro-Fuzzy System, Neural Computing, Vol. 19, No. 6, Pages 1656-1680.

Yaochu Jin, (2003), Advanced Fuzzy Systems Design and Applications, vol: 112, pp- 1-25.




How to Cite

Ahmad, F., Darbari, M., & Asthana, R. (2015). Different Approaches of Soft Computing Techniques (Inference System) which are used in Clinical Decision Support System for Risk based Prioritization. Asian Journal of Computer and Information Systems, 3(1). Retrieved from https://ajouronline.com/index.php/AJCIS/article/view/2264