INTELLIGENT AND FAST RECOGNITION OF HEART DISEASE BASED ON SYNERGY OF LINEAR NEURAL NETWORK AND LOGISTIC REGRESSION MODEL

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HERAVI MOJTABA; SETAYESHI SAEED

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Paper Information

Journal: JOURNAL OF MAZANDARAN UNIVERSITY OF MEDICAL SCIENCES Year:2014 | Volume:24 | Issue:112 Page(s): 78-87

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Title

INTELLIGENT AND FAST RECOGNITION OF HEART DISEASE BASED ON SYNERGY OF LINEAR NEURAL NETWORK AND LOGISTIC REGRESSION MODEL

Author(s)

HERAVI MOJTABA | SETAYESHI SAEED | Issue Writer Certificate

Keywords

HEART DISEASES DIAGNOSISQ2

BIOMEDICAL ENGINEERINGQ2

PATTERN RECOGNITIONQ2

MACHINE LEARNINGQ1

ARTIFICIAL NEURAL NETWORKQ2

SINGLE LAYER PERCEPTRONQ3

LOGISTIC REGRESSIONQ2

Abstract

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.

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APA: Copy

HERAVI, MOJTABA, & SETAYESHI, SAEED. (2014). INTELLIGENT AND FAST RECOGNITION OF HEART DISEASE BASED ON SYNERGY OF LINEAR NEURAL NETWORK AND LOGISTIC REGRESSION MODEL. JOURNAL OF MAZANDARAN UNIVERSITY OF MEDICAL SCIENCES, 24(112), 78-87. SID. https://sid.ir/paper/46214/en

Vancouver: Copy

HERAVI MOJTABA, SETAYESHI SAEED. INTELLIGENT AND FAST RECOGNITION OF HEART DISEASE BASED ON SYNERGY OF LINEAR NEURAL NETWORK AND LOGISTIC REGRESSION MODEL. JOURNAL OF MAZANDARAN UNIVERSITY OF MEDICAL SCIENCES[Internet]. 2014;24(112):78-87. Available from: https://sid.ir/paper/46214/en

IEEE: Copy

MOJTABA HERAVI, and SAEED SETAYESHI, “INTELLIGENT AND FAST RECOGNITION OF HEART DISEASE BASED ON SYNERGY OF LINEAR NEURAL NETWORK AND LOGISTIC REGRESSION MODEL,” JOURNAL OF MAZANDARAN UNIVERSITY OF MEDICAL SCIENCES, vol. 24, no. 112, pp. 78–87, 2014, [Online]. Available: https://sid.ir/paper/46214/en

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