Comparative analysis of intelligent hybrid systems for detection of PIMA indian diabetes

The past few years have seen a lot of applications of Hybrid Soft Computing approaches that seem to have completely replaced the traditional uni-system approaches. The added abilities that come from the hybrid approaches motivate their use in every system. Bio-Medical Engineering is yet another field which has seen a major change in he past few years. We find various new approaches being applied to this field as well as many new models being proposed. At this juncture, we study the effectiveness of various new hybrid approaches in the field of Bio-medicals in this paper. PIMA Indian database has been used for this purpose from the UCI Machine Learning Repository. The basic aim is to compare the various hybrid approaches from the recent literature and compare their performances. We have chosen 3 major Hybrid Systems and standard Back Propagation Algorithm for this purpose. These are Adaptive Neuro Fuzzy Inference Systems, Ensembles and Evolutionary Artificial Neural Networks. We also try to explain the results from our theoretical understanding of the individual Hybrid Systems.

[1]  Fevzullah Temurtas,et al.  A comparative study on diabetes disease diagnosis using neural networks , 2009, Expert Syst. Appl..

[2]  World Congress on Nature & Biologically Inspired Computing, NaBIC 2009, 9-11 December 2009, Coimbatore, India , 2009, NaBIC.

[3]  Antonio Ciampi,et al.  A New Approach to Training Back-propagation Artiÿcial Neural Networks: Empirical Evaluation on Ten Data Sets from Clinical Studies , 2022 .

[4]  Horst Bunke,et al.  Hybrid methods in pattern recognition , 1987 .

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

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

[7]  K. G. Srinivasa,et al.  A self-adaptive migration model genetic algorithm for data mining applications , 2007, Inf. Sci..

[8]  Q. Henry Wu,et al.  A Group Search Optimizer for Neural Network Training , 2006, ICCSA.

[9]  John H. Lilly,et al.  Evolutionary design of a fuzzy classifier from data , 2004, IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics).

[10]  Marijke F. Augusteijn,et al.  Evolving transfer functions for artificial neural networks , 2003, Neural Computing & Applications.

[11]  M. Teshnehlab,et al.  Classification on Diabetes Mellitus Data-set Based-on Artificial Neural Networks and ANFIS , 2008 .

[12]  Carla Purdy,et al.  Hybrid intelligent systems for pattern recognition and signal processing , 2004 .

[13]  Anupam Shukla,et al.  Diagnosis of Epilepsy Disorders Using Artificial Neural Networks , 2009, ISNN.

[14]  辛 太廣,et al.  Johns Hopkins University , 1895, The Biblical World.

[15]  Anupam Shukla,et al.  Knowledge Based Approach for Diagnosis of Breast Cancer , 2009, 2009 IEEE International Advance Computing Conference.