Application of multilayer perceptron neural networks and support vector machines in classification of healthcare data

A large volume of data is steadily produced by the healthcare industry on daily basis. Data mining and machine learning approaches are two effective techniques applicable for data analysis and finding the hidden patterns which can be utilized for medical decision making. As the decisions in medical field are dealing with patient outcome, a high level of accuracy in data mining is needed. In this paper a comparison between implemented multilayer perceptron neural networks and support vector machine on heart diseases dataset is conducted. We have analyzed the effectiveness of support vector machine in classification, using a dataset of 303 patients. Our results show that support vector machine is able to classify more accurately.

[1]  Soni Jyoti,et al.  Predictive Data Mining for Medical Diagnosis: An Overview of Heart Disease Prediction , 2011 .

[2]  A. Bachelor.  GLOSSARY OF TERMS GLOSSARY OF TERMS , 2010 .

[3]  Ling Liu,et al.  From Data Privacy to Location Privacy: Models and Algorithms , 2007, VLDB.

[4]  Lucila Ohno-Machado,et al.  Logistic regression and artificial neural network classification models: a methodology review , 2002, J. Biomed. Informatics.

[5]  David H. Wolpert,et al.  No free lunch theorems for optimization , 1997, IEEE Trans. Evol. Comput..

[6]  Keinosuke Fukunaga,et al.  Introduction to Statistical Pattern Recognition , 1972 .

[7]  Nicos G. Pavlidis,et al.  Minimum Density Hyperplanes , 2015, J. Mach. Learn. Res..

[8]  Paul M. Thompson,et al.  Feature selection improves the accuracy of classifying Alzheimer disease using diffusion tensor images , 2015, 2015 IEEE 12th International Symposium on Biomedical Imaging (ISBI).

[9]  Sulabha S. Apte,et al.  A Data Mining Approach for Prediction of Heart Disease Using Neural Networks , 2012 .

[10]  David G. Stork,et al.  Pattern Classification , 1973 .

[11]  Chin-Teng Lin,et al.  Support-vector-based fuzzy neural network for pattern classification , 2006, IEEE Transactions on Fuzzy Systems.

[12]  Fiaz Majeed,et al.  Data Mining in Healthcare for Heart Diseases , 2015 .

[13]  J. Hertzberg,et al.  Artificial Neural Network-Based Method of Screening Heart Murmurs in Children , 2001, Circulation.

[14]  H. Bazargan,et al.  A hybrid method for estimating the process change point using support vector machine and fuzzy statistical clustering , 2016, Appl. Soft Comput..

[15]  Michael Y. Hu,et al.  Forecasting with artificial neural networks: The state of the art , 1997 .

[16]  João Pedro Pedroso,et al.  Tree Search and Simulation , 2015 .

[17]  Daniel Kifer,et al.  Unifying Adversarial Training Algorithms with Flexible Deep Data Gradient Regularization , 2016, ArXiv.

[18]  Ian H. Witten,et al.  The WEKA data mining software: an update , 2009, SKDD.

[19]  Sulabha S. Apte,et al.  Improved Study of Heart Disease Prediction System using Data Mining Classification Techniques , 2012 .

[20]  Claudio Delrieux,et al.  Methodology for classification of geographical features with remote sensing images: Application to tidal flats , 2016 .

[21]  Malik Yousef,et al.  One-Class SVMs for Document Classification , 2002, J. Mach. Learn. Res..

[22]  D.R. Hush,et al.  Progress in supervised neural networks , 1993, IEEE Signal Processing Magazine.

[23]  Shigeo Abe.  Support Vector Machines for Pattern Classification , 2010, Advances in Pattern Recognition.

[24]  A. Atiya,et al.  Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond , 2005, IEEE Transactions on Neural Networks.