%0 Journal Article %1 serdaraydin2016comparison %A Serdar AYDIN, Meysam Ahanpanjeh %A Mohabbatiyan, Sogol %D 2016 %E E.V.Krishnamurthy, Australian National University, Australia %J International Journal on Computational Science & Applications (IJCSA) %K AdaBoost, Bagging, Bayes, Data Disease, Forest, Heart IBK,NNge Mining, Naive Network, RBF Random %N 1 %P 1-15 %T Comparison and Evaluation Data Mining Techniques in the Diagnosis of Heart Disease %U http://wireilla.com/papers/ijcsa/V6N1/6116ijcsa01.pdf %V 6 %X Heart disease is one of the biggest health problems in the world because of high mortality and morbidity caused by the disease. The use of data mining on medical data brought valuable and effective life achievements and can enhance medical knowledge to make necessary decisions. Data mining plays an important role in the field of medical science to solve health problems and diagnose ailments in critical conditions and in normal conditions. For this reason, in this paper, data mining techniques are used to diagnose heart disease from a dataset that includes 200 samples from different patients. Techniques used to diagnose heart disease include Bagging, AdaBoostM1, Random Forest, Naive Bayes, RBF Network, IBK, and NNge that all the techniques used to diagnose heart disease use Weka tool. Then these techniques are compared to determine which is more accurate in the diagnosis of heart disease that according to the results, it was found that the RBF Network with the accuracy of 88.2% is the most accurate classification in the diagnosis of heart disease.

@article{serdaraydin2016comparison, abstract = {Heart disease is one of the biggest health problems in the world because of high mortality and morbidity caused by the disease. The use of data mining on medical data brought valuable and effective life achievements and can enhance medical knowledge to make necessary decisions. Data mining plays an important role in the field of medical science to solve health problems and diagnose ailments in critical conditions and in normal conditions. For this reason, in this paper, data mining techniques are used to diagnose heart disease from a dataset that includes 200 samples from different patients. Techniques used to diagnose heart disease include Bagging, AdaBoostM1, Random Forest, Naive Bayes, RBF Network, IBK, and NNge that all the techniques used to diagnose heart disease use Weka tool. Then these techniques are compared to determine which is more accurate in the diagnosis of heart disease that according to the results, it was found that the RBF Network with the accuracy of 88.2% is the most accurate classification in the diagnosis of heart disease. }, added-at = {2017-02-28T00:58:16.000+0100}, author = {Serdar AYDIN, Meysam Ahanpanjeh and Mohabbatiyan, Sogol}, biburl = {https://www.bibsonomy.org/bibtex/2e35931e561a0a0921c780fc4ec291908/laimbee}, editor = {{E.V.Krishnamurthy, Australian National University}, Australia}, interhash = {f3913d065e96e8d496d01a10e9e6a02a}, intrahash = {e35931e561a0a0921c780fc4ec291908}, journal = {International Journal on Computational Science & Applications (IJCSA) }, keywords = {AdaBoost, Bagging, Bayes, Data Disease, Forest, Heart IBK,NNge Mining, Naive Network, RBF Random}, month = {February }, number = 1, pages = {1-15}, timestamp = {2017-02-28T00:58:16.000+0100}, title = {Comparison and Evaluation Data Mining Techniques in the Diagnosis of Heart Disease}, url = {http://wireilla.com/papers/ijcsa/V6N1/6116ijcsa01.pdf}, volume = 6, year = 2016 }