Association Rules Mining Based Clinical Observations

Healthcare institutes enrich the repository of patients' disease related information in an increasing manner which could have been more useful by carrying out relational analysis. Data mining algorithms are proven to be quite useful in exploring useful correlations from larger data repositories. In this paper we have implemented Association Rules mining based a novel idea for finding co-occurrences of diseases carried by a patient using the healthcare repository. We have developed a system-prototype for Clinical State Correlation Prediction (CSCP) which extracts data from patients' healthcare database, transforms the OLTP data into a Data Warehouse by generating association rules. The CSCP system helps reveal relations among the diseases. The CSCP system predicts the correlation(s) among primary disease (the disease for which the patient visits the doctor) and secondary disease/s (which is/are other associated disease/s carried by the same patient having the primary disease).

[1]  Reza Sherafat Kazemzadeh,et al.  Interoperability of Data and Knowledge in Distributed Health Care Systems , 2005, 13th IEEE International Workshop on Software Technology and Engineering Practice (STEP'05).

[2]  B G Celler,et al.  Web-based acquisition, storage, and retrieval of biomedical signals. , 2001, IEEE engineering in medicine and biology magazine : the quarterly magazine of the Engineering in Medicine & Biology Society.

[3]  Joseph Y. Lo,et al.  New results in breast cancer classification obtained from an evolutionary computation/adaptive boosting hybrid using mammogram and history data , 2001, SMCia/01. Proceedings of the 2001 IEEE Mountain Workshop on Soft Computing in Industrial Applications (Cat. No.01EX504).

[4]  Carlos Ordonez,et al.  Discovering Interesting Association Rules in Medical Data , 2000, ACM SIGMOD Workshop on Research Issues in Data Mining and Knowledge Discovery.

[5]  Michael Kidd,et al.  Healthcare Applications based on Software Agents , 2001, MedInfo.

[6]  Leonid Churilov,et al.  Improving risk grouping rules for prostate cancer patients with optimization , 2004, 37th Annual Hawaii International Conference on System Sciences, 2004. Proceedings of the.

[7]  Richard J. Cleary.  Applied Data Mining: Statistical Methods for Business and Industry , 2006 .

[8]  Vibha Anand,et al.  Child Health Improvement through Computer Automation: The CHICA System , 2004, MedInfo.

[9]  Osmar R. Zaïane,et al.  Mammography Classification By an Association Rule-based Classifier , 2002, MDM/KDD.

[10]  David F. Lobach,et al.  Medical data mining: knowledge discovery in a clinical data warehouse , 1997, AMIA.

[11]  H. Takeuchi,et al.  Automated Healthcare Data Mining Based on a Personal Dynamic Healthcare System , 2006, 2006 International Conference of the IEEE Engineering in Medicine and Biology Society.

[12]  M. H. Margahny,et al.  FAST ALGORITHM FOR MINING ASSOCIATION RULES , 2014 .

[13]  Sergio A. Alvarez,et al.  Mining associations over human sleep time series , 2005, 18th IEEE Symposium on Computer-Based Medical Systems (CBMS'05).

[14]  A. Vlahou,et al.  Diagnosis of Ovarian Cancer Using Decision Tree Classification of Mass Spectral Data , 2003, Journal of biomedicine & biotechnology.

[15]  H. Mannila,et al.  Data mining: machine learning, statistics, and databases , 1996, Proceedings of 8th International Conference on Scientific and Statistical Data Base Management.

[16]  Jerzy W. Grzymala-Busse,et al.  Data mining methods supporting diagnosis of melanoma , 2005, 18th IEEE Symposium on Computer-Based Medical Systems (CBMS'05).