A New Hybrid Method for Improving the Performance of Myocardial Infarction Prediction
作者： Atefeh Daraie , Hojatollah Hamidi
DOI:
关键词: Artificial intelligence 、 Decision tree 、 C4.5 algorithm 、 Statistical classification 、 AdaBoost 、 Genetic algorithm 、 Data set 、 Naive Bayes classifier 、 Feature selection 、 Medical emergency 、 Machine learning 、 Medicine
摘要: Introduction: Myocardial Infarction, also known as heart attack, normally occurs due to such causes smoking, family history, diabetes, and so on. It is recognized one of the leading death in world. Therefore, present study aimed evaluate performance classification models order predict using a feature selection method that includes Forward Selection Genetic Algorithm. Materials & Methods: The Infarction data set used this contains information related 519 visitors Shahid Madani Specialized Hospital Khorramabad, Iran. This 33 features. proposed hybrid enhance algorithms. first step selects features Selection. At second step, selected were given genetic algorithm, select best Classification algorithms entail Ada Boost, Naive Bayes, J48 decision tree simpleCART are applied with features, for predicting Infarction. Results: results have been achieved after applying method, which obtained via accuracies 96.53% 96.34%, respectively. Conclusion: Based on results, performances improved. So, along seem be considered confident respect
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