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Automatic diagnosis of ischemic heart disease using combined classifiers

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Abstract

The cardiovascular disease risk in modern industrial societies and living with sedentary lifestyles, high-calorie meals, and mental stress is on the rise. Methods of diagnosing the disease, which is based on clinical examinations and tests, are time-consuming and costly, and can also involve human error. Therefore, data mining methods have been used in recent years, each of which has its advantages and disadvantages. Due to the variety of features related to patient data, the use of a classifier alone cannot cover all the hidden sides of the problem. Thus, in the proposed method, a combined cascading learning model is used, which consists of two levels. In the first level, the Bayesian classifier is used, which adds two characteristics of the possibility of being sick or not to the data. In the second level, the decision tree and RIPPER classifiers are used in parallel. The model is based on the heart data set. The evaluation results based on the accuracy, recall, and precision parameters show that the proposed method compared to Miao and Yakala methods based on the accuracy parameter has improved performance by 9% and 2%, respectively.

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Authors and Affiliations

  1. Department of Computer Engineering, University of Arvandan khoramshahr, Khoramshahr, Iran

    Alireza Manishdavi

  2. Department of Computer Engineering, Ramhormoz Branch, Islamic Azad University, Ramhormoz, Iran

    Mahnaz Rafie

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  1. Alireza Manishdavi
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  2. Mahnaz Rafie
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Correspondence to Mahnaz Rafie.

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Manishdavi, A., Rafie, M. Automatic diagnosis of ischemic heart disease using combined classifiers. Multimed Tools Appl 82, 33135–33159 (2023). https://doi.org/10.1007/s11042-023-14834-y

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  • DOI: https://doi.org/10.1007/s11042-023-14834-y

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