An Efficient Algorithm For Predicting Survival Of Medical Data (Epsa)

By:
Prof Hemalatha Sekar,
V V Gomathi,
Prof. Karthikeyan Subramanian
To add a paper, Login.

Association Rule Mining is one of the most popular Data Mining task. Extraction of frequent itemset is an important problem in Data Mining. Pincer-Search algorithm has developed by Dao-l Lin et al and Zvi M. Kedem in the year 1998. Pincer-Search Algorithm has been incorporated which combines both the bottom-up and the top-down searches. So the Pincer-Search Algorithm performs well when some maximal frequent itemsets are long.

In this paper Enhanced Pincer-Search Algorithm is applied to retrieve useful information from maximal frequent itemsets and predict fruitful interesting results. This enhanced Pincer- Search Algorithm (EPSA) was implemented with Cardiatic Patient’s database. The opportunity to analyse medical events in a patient life is described briefly The support value is increased to the Live Patients and compared with the death patients. From increasing the support values, the live patient reaches the value of the death patient and suggestions are given to increase the patient’s life days.


Keywords: Pincer-search Algorithm, EPSA, MFS, Cardiotic, Patients
Stream: Knowledge and Technology
Presentation Type: Paper Presentation in English
Paper: Efficient Algorithm for Predicting Survival of Medical Data (Epsa), An


Prof Hemalatha Sekar

Lecturer, Department of Computer Science, Karpagam Arts and Science College
India

More than 3 years experience in teaching. 10 Papers are published in various journals. Interesting area bioinformatics and algorithm analysis

V V Gomathi

Karpagam Arts and Science Collelge,Coimbatore, Departmet of Computer Science, Karpagam
India

2years of teaching. Attended more than 5 conferences.

Prof. Karthikeyan Subramanian

Senior Grade Lecturer, Department of Computer Science, Karpagam Arts and Science College, Coimbatore
India


Ref: T06P0340