Handling Large Medical Data Sets for Disease Detection

Computational Intelligence has given rise to automation in numerous spheres. Bio-medical engineering is one of these spheres where the computationally intelligent systems have automated the task of detection of diseases. These systems are able to affectively detect the presence or absence of any disease. These systems take the various parameters as inputs that can potentially affect the decision regarding the presence or absence of disease. The system analyzes these inputs and then makes its decision. Every input or parameter affects the decision of the system to some extent. Some parameters are very important whereas the others may behave rather passive in nature. Ideally the larger the number of parameters, the more is the chance of detection of the disease. This is ABSTRACT

[1]  Sanjika Hewavitharana,et al.  Off-Line Sinhala Handwriting Recognition Using Hidden Markov Models , 2002, ICVGIP.

[2]  Abdullah Al Mamun,et al.  Training neural networks for classification using growth probability-based evolution , 2008, Neurocomputing.

[3]  Bidyut Baran Chaudhuri,et al.  Efficient training and improved performance of multilayer perceptron in pattern classification , 2000, Neurocomputing.

[4]  D. Sharma,et al.  Learning from Ensembles: Using Artificial Neural Network Ensemble for Medical Outcomes Prediction , 2006, 2006 Innovations in Information Technology.

[5]  C. W. Tao,et al.  A New Neuro-Fuzzy Classifier with Application to On-Line Face Detection and Recognition , 2000, J. VLSI Signal Process..

[6]  Ritu Tiwari,et al.  Fuzzy Neuro Systems for Machine Learning for Large Data Sets , 2009, 2009 IEEE International Advance Computing Conference.

[7]  Euripidis Glavas,et al.  Neural network construction and training using grammatical evolution , 2008, Neurocomputing.

[8]  Ziad O. Abu-Faraj,et al.  Handbook of Research on Biomedical Engineering Education and Advanced Bioengineering Learning: Interdisciplinary Concepts , 2012 .

[9]  Hee Hyol Lee,et al.  A neuro-fuzzy classifier for land cover classification , 1999, FUZZ-IEEE'99. 1999 IEEE International Fuzzy Systems. Conference Proceedings (Cat. No.99CH36315).

[10]  Subana Shanmuganathan,et al.  Data-Mining Techniques for an Analysis Of Non-Conventional Methodologies: Deciphering of Alternative Medicine , 2010 .

[11]  Pedro Antonio Gutiérrez,et al.  Evolutionary product-unit neural networks classifiers , 2008, Neurocomputing.

[12]  Neil Davey,et al.  Using a genetic algorithm to investigate efficient connectivity in associative memories , 2009, Neurocomputing.

[13]  Alfredo Álvarez,et al.  Sleep stage classification using fuzzy sets and machine learning techniques , 2004, Neurocomputing.

[14]  Haitao Liu,et al.  Feature selection for handwritten Chinese character recognition based on genetic algorithms , 1998, SMC'98 Conference Proceedings. 1998 IEEE International Conference on Systems, Man, and Cybernetics (Cat. No.98CH36218).

[15]  Meng Joo Er,et al.  A novel framework for automatic generation of fuzzy neural networks , 2008, Neurocomputing.

[16]  Natarajan Sriraam,et al.  Statistical Analysis of Spectral Entropy Features for the Detection of Alcoholics Based on Elecroencephalogram (EEG) Signals , 2012 .

[17]  Kazuyuki Murase,et al.  Single-layered complex-valued neural network for real-valued classification problems , 2009, Neurocomputing.

[18]  João Paulo Carmo,et al.  Optical Fibers on Medical Instrumentation: A Review , 2013 .

[19]  Ranko Stevanović,et al.  Handbook of Research on Informatics in Healthcare and Biomedicine , 2006 .

[20]  Jürgen Schmidhuber,et al.  Unconstrained On-line Handwriting Recognition with Recurrent Neural Networks , 2007, NIPS.

[21]  Yi-Chung Hu,et al.  Nonadditive grey single-layer perceptron with Choquet integral for pattern classification problems using genetic algorithms , 2008, Neurocomputing.

[22]  Loris Nanni,et al.  Particle swarm optimization for prototype reduction , 2009, Neurocomputing.

[23]  Armando Vieira,et al.  A training algorithm for classification of high-dimensional data , 2003, Neurocomputing.

[24]  Sorin Draghici,et al.  A Neural Network Based Artificial Vision System for Licence Plate Recognition , 1997, Int. J. Neural Syst..

[25]  Yuko Mizuno-Matsumoto,et al.  Non-Linear Analysis of Plethysmograms and the Effect of Communication on Dementia in Elderly Individuals , 2011 .

[26]  B. Daruka Prasad,et al.  Magnetic Nano Particles for Medical Applications , 2013 .

[27]  Javier de Lope,et al.  Hybridizing evolutionary computation and reinforcement learning for the design of almost universal controllers for autonomous robots , 2009 .

[28]  Cheng-Jian Lin,et al.  The design of neuro-fuzzy networks using particle swarm optimization and recursive singular value decomposition , 2007, Neurocomputing.

[29]  R. J. Kuo,et al.  Continuous genetic algorithm-based fuzzy neural network for learning fuzzy IF-THEN rules , 2008, Neurocomputing.

[30]  Michael O'Neill,et al.  Grammatical Evolution: Evolving Programs for an Arbitrary Language , 1998, EuroGP.

[31]  Conor Ryan,et al.  Grammatical Evolution , 2001, Genetic Programming Series.

[32]  Bill Ag. Drougas.  Virtual Reality Simulation in Human Applied Kinetics and Ergo Physiology , 2008 .

[33]  Subhash C. Kak,et al.  On Generalization by Neural Networks , 1998, Inf. Sci..

[34]  Raj Gururajan,et al.  Biomedical Knowledge Management: Infrastructures and Processes for E-Health Systems , 2010 .

[35]  Andriani Daskalaki,et al.  Potential Benefits and Challenges of Computer-Based Learning in Health , 2006 .

[36]  Cheng-Jian Lin,et al.  An entropy-based quantum neuro-fuzzy inference system for classification applications , 2007, Neurocomputing.

[37]  Chien-Hsing Chou,et al.  A New Approach to Fuzzy Classifier Systems and Its Application in Self-generating Neuro-fuzzy Systems , 2022 .

[38]  Gernot A. Fink,et al.  Unsupervised Estimation of Writing Style Models for Improved Unconstrained Off-line Handwriting Recognition , 2006 .