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Background and purpose: Diseases have been the greatest threat for human being along the history. ‎Heart disease (HD) has gained special attention in medical studies. Recently studying on classification and ‎diagnosis of HD as a key topic and a lot of researches have been done in order to increase precise and reduce ‎error in this type of decisions. With development of intelligent learning systems, these systems have played a ‎great role in reducing the error of decision support systems (DSS).‎ Materials and methods: In this study, a simple hybrid model of logistic regression and single-layer ‎perceptron neural network was presented which was trained with four-different learning rules (separately). ‎The model for improving the classification and patterns recognition of HD has been used on clinical data of ‎‎270 patients from the Cleveland Clinic (UCI website). This method has been used in statistical data ‎normalization and detection of noisy data, network training with only 20% of the data exist was performed. ‎The model has been implemented in MATLAB.‎ Results: The mean-error of the proposed model on the total dataset was 11.11%, which was achieved a ‎significant improvement compared to recent similar methods. In addition, the results showed that the proposed ‎approach was very capable in dealing with noise in the data‏.‏ Conclusion: The results clearly showed that the linear proposed technique had large impact on ‎reducing the error in the classification and identification of patients more accurately in a shorter time than ‎conventional methods and complex nonlinear. The method can help physicians for early detection of disease ‎or as a DSS.‎

Keywords: Heart diseases diagnosis, Biomedical engineering, Pattern recognition, Machine learning, Artificial neural ‎network, Single layer perceptron, Logistic regression

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