New hybrid method for heart disease diagnosis utilizing optimization algorithm in feature selection

The number and size of medical databases are rapidly increasing, and the advanced models of data mining techniques could help physicians to make efficient and applicable decisions. The challenges of heart disease data include the feature selection, the number of the samples; imbalance of the samples, lack of magnitude for some features, etc. This study mainly focuses on the feature selection improvement and decreasing the numbers of the features. In this study, imperialist competitive algorithm with meta-heuristic approach is suggested in order to select prominent features of the heart disease. This algorithm can provide a more optimal response for feature selection toward genetic in compare with other optimization algorithms. Also, the K-nearest neighbor algorithm is used for the classification. Evaluation result shows that by using the proposed algorithm, the accuracy of feature selection technique has been improved.

[1]  Shweta Kharya,et al.  Using data mining techniques for diagnosis and prognosis of cancer disease , 2012, ArXiv.

[2]  Imran Khan,et al.  Feature extraction through parallel Probabilistic Principal Component Analysis for heart disease diagnosis , 2017 .

[3]  V. Ranjani,et al.  Data Mining Applications In Healthcare Sector: A Study , 2013 .

[4]  Arvind Sharma,et al.  Predicting the Number of Blood Donors through their Age and Blood Group by using Data Mining Tool , 2012 .

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

[6]  Asha Rajkumar,et al.  Diagonsis of Heaer Disease using Datamining Algorithm , 2010 .

[7]  Harry Zhang,et al.  The Optimality of Naive Bayes , 2004, FLAIRS.

[8]  H. Chubb,et al.  The use of Z-scores in paediatric cardiology , 2012, Annals of Pediatric Cardiology.

[9]  Bulusu Lakshmana Deekshatulu,et al.  Classification of Heart Disease Using K- Nearest Neighbor and Genetic Algorithm , 2015, ArXiv.

[10]  Elpida T. Keravnou,et al.  Combining Naive Bayes Classifiers with Temporal Association Rules for Coronary Heart Disease Diagnosis , 2016, 2016 IEEE International Conference on Healthcare Informatics (ICHI).

[11]  Sellappan Palaniappan,et al.  Intelligent heart disease prediction system using data mining techniques , 2008, 2008 IEEE/ACS International Conference on Computer Systems and Applications.

[12]  M. Balamurugan,et al.  A Survey on Heart Disease Prediction System Using Data Mining Techniques , 2014 .

[13]  Chanin Nantasenamat,et al.  Data mining of magnetocardiograms for prediction of ischemic heart disease , 2010, EXCLI journal.

[14]  Asma Ghandeharioun,et al.  Diagnosis of Coronary Arteries Stenosis Using Data Mining , 2012, Journal of medical signals and sensors.

[15]  Amit Chhabra,et al.  Predicting Primary Tumors using Multiclass Classifier Approach of Data Mining , 2014 .

[16]  T. Mulligan,et al.  Analysis of Echo Counting Data: A Model , 1984 .

[17]  H. Benamer,et al.  Frequency and clinical patterns of stroke in Iran - Systematic and critical review , 2010, BMC neurology.

[18]  Ali Taghipour,et al.  hs-CRP is strongly associated with coronary heart disease (CHD): A data mining approach using decision tree algorithm , 2017, Comput. Methods Programs Biomed..

[19]  Caro Lucas,et al.  Imperialist competitive algorithm: An algorithm for optimization inspired by imperialistic competition , 2007, 2007 IEEE Congress on Evolutionary Computation.

[20]  Majid Ghonji Feshki,et al.  Improving the heart disease diagnosis by evolutionary algorithm of PSO and Feed Forward Neural Network , 2016, 2016 Artificial Intelligence and Robotics (IRANOPEN).