International Transaction Journal of Engineering, Management, & Applied Sciences & Technologies


:: International Transaction Journal of Engineering, Management, & Applied Sciences & Technologies

ISSN 2228-9860
eISSN 1906-9642


Vol.11(15) (2020)

  • Efficient Diagnostic Cardiac System using Machine Learning Approach

    Mujtaba Ashraf Qureshi (Department of Information Technology, Mewar University, Chittorgarh (Raj), INDIA),
    Azad Kumar Shrivastava (Department of Computer Science, Mewar University, Chittorgarh (Raj), INDIA).

    Disciplinary: Computer and Information Technology, Cardiology (Cardiovascular Health and Disease).

    ➤ FullText

    DOI: 10.14456/ITJEMAST.2020.294

    Keywords: Data mining; neural networks; feature selection; WEKA tool; Cardiovascular disease prediction; Cardiac disease prediction.

    Heart disease is considered one of the ultimate threats to human life. To predict cardiac diseases in the early stages has become a challenge to medical science. Machine learning has acted as a rescuer to assist and develop various cardiac diagnostic systems. Data mining techniques mostly used as a synonym to machine learning plays an important role to mine useful knowledge. However, machine learning (ML) emphasis more on the prediction of diverse diseases. In this research work, three models are devised to predict cardiovascular diseases using artificial neural networks. Models are devised based on the application of a different number of hidden layers. The backpropagation algorithm is used to calculate the desired value by the adjustment of weights of the neurons in the network. In the very last stage of the experimental work performance measures of the three devised models are compared to reach the most efficient model.

    Paper ID: 11A15F

    Cite this article:

    Qureshi, M.A., Shrivastava, A.K. (2020). Efficient Diagnostic Cardiac System using Machine Learning Approach.International Transaction Journal of Engineering, Management, & Applied Sciences & Technologies, 11(15), 11A15F, 1-8.


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