enhanced prediction of heart stipulation using data mining
M.Thilagavathi,P.Thirugnanam
Published in International Journal of Advanced Research in Computer Science Engineering and Information Technology
ISSN: 2321-3337 Impact Factor:1.521 Volume:4 Issue:3 Year: 22 March,2016 Pages:449-455
Abstract
Myocardial Infarction is the most deadly cause in many developed countries. In that MI risk prediction model uses cardiovascular health study dataset by clinicians. The MI prediction model study the dataset regularly in equal intervals of time over 6years of span and only for the patient aged above 65. The main objective of the proposed model is to predict instantly with the input dataset contains normal ranges, patient’s activity and symptoms. These factors are considered which not mind at all. The user can also able to identify the factors of heart disease and acquire prior knowledge about prediction. The K-means clustering and genetic algorithms are used to cluster and compare with the normal ranges, and previous records of the patient. Cluster analysis is well performed in the K-means clustering. The system is dependable, resilient, web based and user-friendly. Thus the proposed system provides the functions to keep the patient aware about the risk of prediction to heart attack.
Kewords
Myocardial infarction, clinical risk prediction, k-means clustering, genetic algorithm
Reference
[1] Jyoti Soni, Ujma Ansari, Dipesh Sharma, Sunita Soni, Predictive Data Mining for Medical Diagnosis: An Overview of Heart Disease Prediction, International Journal of Computer Applications (0975 – 8887), Volume 17– No.8, March 2011 [2] N. Aditya Sundar, P. Pushpa Latha, M. Rama Chandra, PERFORMANCE ANALYSIS OF CLASSIFICATION DATA MINING TECHNIQUES OVER HEART DISEASE DATA BASE, ADITYA SUNDAR* et al. ISSN: 2250–3676, [IJESAT] INTERNATIONAL JOURNAL OF ENGINEERING SCIENCE & ADVANCED TECHNOLOGY Volume-2, Issue-3, 470 – 478
