SURVEY ON PREDICTION OF HEART MORBIDITY USING DATA MINING TECHNIQUES

Data mining is the non trivial extraction of implicit, previously unknown and potentially useful information from data. Data mining technology provides a user- oriented approach to novel and hidden patterns in the data. This paper presents about the various existing techniques, the issues and challenges associated with them. The discovered knowledge can be used by the healthcare administrators to improve the quality of service and also used by the medical practitioners to reduce the number of adverse drug effect, to suggest less expensive therapeutically equivalent alternatives. In this paper we discuss the popular data mining techniques namely, Decision Trees, Naive Bayes and Neural Network that are used for prediction of disease.

[1]  Joseph P. Noonan,et al.  Fuzzy Kohonen Network for the Classification of Transients Using the Wavelet Transform for Feature Extraction , 1995, Inf. Sci..

[2]  Keith J Zullig,et al.  Higher coronary heart disease and heart attack morbidity in Appalachian coal mining regions. , 2009, Preventive medicine.

[3]  Alex Alves Freitas,et al.  Mining Very Large Databases with Parallel Processing , 1997, The Kluwer International Series on Advances in Database Systems.

[4]  T. Sapatinas,et al.  Wavelet Analysis and its Statistical Applications , 2000 .

[5]  Paul Gray,et al.  Decision support in the data warehouse , 1997 .

[6]  Liangxiao Jiang,et al.  One Dependence Augmented Naive Bayes , 2005, ADMA.

[7]  Yoav Freund,et al.  A decision-theoretic generalization of on-line learning and an application to boosting , 1995, EuroCOLT.

[8]  Shien-Ming Wu,et al.  Time series and system analysis with applications , 1983 .

[9]  Christos Faloutsos,et al.  Efficient Similarity Search In Sequence Databases , 1993, FODO.

[10]  Richard E. Neapolitan,et al.  Learning Bayesian networks , 2007, KDD '07.

[11]  Carlo Batini,et al.  A Data Quality Methodology for Heterogeneous Data , 2011 .

[12]  Richard J. Povinelli,et al.  DATA MINING OF MULTIPLE NONSTATIONARY TIME SERIES , 1999 .

[13]  Andreas Rudolph,et al.  Techniques of Cluster Algorithms in Data Mining , 2002, Data Mining and Knowledge Discovery.

[14]  Philipp Slusallek,et al.  Introduction to real-time ray tracing , 2005, SIGGRAPH Courses.

[15]  Lawrence Carin,et al.  A Bayesian approach to joint feature selection and classifier design , 2004, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[16]  Rolf Stadler,et al.  Discovering Data Mining: From Concept to Implementation , 1997 .

[17]  Vladimir N. Vapnik,et al.  The Nature of Statistical Learning Theory , 2000, Statistics for Engineering and Information Science.

[18]  Lars Kai Hansen,et al.  Imputating missing values in diary records of sun-exposure study , 2001, Neural Networks for Signal Processing XI: Proceedings of the 2001 IEEE Signal Processing Society Workshop (IEEE Cat. No.01TH8584).

[19]  Gwilym M. Jenkins,et al.  Time series analysis, forecasting and control , 1971 .

[20]  F F Nobre,et al.  A monitoring system to detect changes in public health surveillance data. , 1994, International journal of epidemiology.

[21]  Gregory Piatetsky-Shapiro,et al.  Knowledge Discovery in Databases: An Overview , 1992, AI Mag..

[22]  George E. P. Box,et al.  Time Series Analysis: Forecasting and Control , 1977 .

[23]  Ron Kohavi,et al.  Feature Selection for Knowledge Discovery and Data Mining , 1998 .

[24]  Philip S. Yu,et al.  A new method for similarity indexing of market basket data , 1999, SIGMOD '99.

[25]  Gwilym M. Jenkins,et al.  Time series analysis, forecasting and control , 1972 .

[26]  TEMPORAL PATTERN ANALYSIS – A NEW ALGORITHM FOR DETECTING PATCH SIZE IN PLANT POPULATIONS , 2010 .

[27]  R Kavitha Kumar,et al.  Attribute Correction - Data Cleaning Using Association Rule and Clustering Methods , 2011 .

[28]  D. Rubin,et al.  Maximum likelihood from incomplete data via the EM - algorithm plus discussions on the paper , 1977 .

[29]  Nir Friedman,et al.  Bayesian Network Classifiers , 1997, Machine Learning.

[30]  Padhraic Smyth,et al.  From Data Mining to Knowledge Discovery in Databases , 1996, AI Mag..

[31]  N Nagarwalla,et al.  A scan statistic with a variable window. , 1996, Statistics in medicine.

[32]  Fernando Pereira,et al.  Aggregate and mixed-order Markov models for statistical language processing , 1997, EMNLP.

[33]  A. Govardhan,et al.  Analysis of coronary heart disease and prediction of heart attack in coal mining regions using data mining techniques , 2010, 2010 5th International Conference on Computer Science & Education.

[34]  Pedro M. Domingos,et al.  On the Optimality of the Simple Bayesian Classifier under Zero-One Loss , 1997, Machine Learning.

[35]  G. C. Tiao,et al.  Consistency Properties of Least Squares Estimates of Autoregressive Parameters in ARMA Models , 1983 .

[36]  John W. Auer,et al.  Linear algebra with applications , 1996 .

[37]  Eamonn Keogh A Fast and Robust Method for Pattern Matching in Time Series Databases , 2012 .