REVIEW OF HEART DISEASE PREDICTION SYSTEM USING DATA MINING AND HYBRID INTELLIGENT TECHNIQUES

The Healthcare industry generally clinical diagnosis is done mostly by doctor's expertise and experience. Computer Aided Decision Support System plays a major role in medical field. With the growing research on heart disease predicting system, it has become important to categories the research outcomes and provides readers with an overview of the existing heart disease prediction techniques in each category. Neural Networks are one of many data mining analytical tools that can be utilized to make predictions for medical data. From the study it is observed that Hybrid Intelligent Algorithm improves the accuracy of the heart disease prediction system. The commonly used techniques for Heart Disease Prediction and their complexities are summarized in this paper.

[1]  V. Sundarapandian,et al.  A NEURO FUZZY EXPERT SYSTEM FOR HEART DISEASE DIAGNOSIS , 2012 .

[2]  Olatubosun Olabode,et al.  Cerebrovascular Accident Attack Classification Using Multilayer Feed Forward Artificial Neural Network with Back Propagation Error , 2012 .

[3]  P. Gayathri,et al.  Effective Analysis and Predictive Model of Stroke Disease using Classification Methods , 2012 .

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

[5]  M Anbarasi,et al.  ENHANCED PREDICTION OF HEART DISEASE WITH FEATURE SUBSET SELECTION USING GENETIC ALGORITHM , 2010 .

[6]  Padmakumari K. N. Anooj,et al.  Clinical decision support system: risk level prediction of heart disease using weighted fuzzy rules and decision tree rules , 2011, Central European Journal of Computer Science.

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

[8]  T. Dent,et al.  Predicting the risk of coronary heart disease I. The use of conventional risk markers. , 2010, Atherosclerosis.

[9]  Hsing-Yi Chang,et al.  Predicting the Risk of Coronary Heart Disease and Diabetes in Taiwan , 2011 .

[10]  Peter Kulchyski.  and , 2015 .

[11]  Jennie Malboeuf.  Algorithm , 1994, Neurology.

[12]  MA. JABBAR,et al.  CLUSTER BASED ASSOCIATION RULE MINING FOR HEART ATTACK PREDICTION , .

[13]  Deepali Chandna,et al.  Diagnosis of Heart Disease Using Data Mining Algorithm , 2014 .

[14]  A. Govardhan,et al.  SURVEY ON PREDICTION OF HEART MORBIDITY USING DATA MINING TECHNIQUES , 2011 .

[15]  D. Shanthi,et al.  Input Feature Selection using Hybrid Neuro-Genetic Approach in the Diagnosis of Stroke Disease , 2008 .

[16]  Obi J.C,et al.  Decision Support System for the Intelligient Identification of Alzheimer using Neuro Fuzzy logic , 2011 .

[17]  K. Usha Rani,et al.  Class : Class 0 : Normal Person . Class 1 : first stroke Class 2 : second stroke Class 3 : end of life Attribute Description , 2011 .

[18]  L. Parthiban,et al.  Intelligent Heart Disease Prediction System Using CANFIS and Genetic Algorithm , 2007 .

[19]  P. K. Anooj,et al.  Clinical decision support system: Risk level prediction of heart disease using weighted fuzzy rules , 2012, J. King Saud Univ. Comput. Inf. Sci..

[20]  Gadadhar Sahoo,et al.  Designing an Artificial Neural Network Model for the Prediction of Thromboembolic Stroke , 2004 .

[21]  Mai Shouman,et al.  Integrating Decision Tree and K-Means Clustering with Different Initial Centroid Selection Methods in the Diagnosis of Heart Disease Patients , 2012 .