Proceedings of the 5th International Conference on Applied Engineering, ICAE 2022, 5 October 2022, Batam, Indonesia

Research Article

Heart Condition Classification using Deep Learning as A Diagnosing Helper

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  • @INPROCEEDINGS{10.4108/eai.5-10-2022.2329543,
        author={Churun In Layyinah and Mohamad Johan Arifin and Riyanto  Sigit and Tita  Karlita and Taufiq  Hidayat},
        title={Heart Condition Classification using Deep Learning as A Diagnosing Helper},
        proceedings={Proceedings of the 5th International Conference on Applied Engineering, ICAE 2022, 5 October 2022, Batam, Indonesia},
        publisher={EAI},
        proceedings_a={ICAE},
        year={2023},
        month={6},
        keywords={heart disease deep learning tracking optical flow classification},
        doi={10.4108/eai.5-10-2022.2329543}
    }
    
  • Churun In Layyinah
    Mohamad Johan Arifin
    Riyanto Sigit
    Tita Karlita
    Taufiq Hidayat
    Year: 2023
    Heart Condition Classification using Deep Learning as A Diagnosing Helper
    ICAE
    EAI
    DOI: 10.4108/eai.5-10-2022.2329543
Churun In Layyinah1,*, Mohamad Johan Arifin1, Riyanto Sigit1, Tita Karlita1, Taufiq Hidayat2
  • 1: Politeknik Elektronika Negeri Surabaya
  • 2: Universitas Airlangga
*Contact email: churuninl@ce.student.pens.ac.id

Abstract

Heart is one of the most vital organ. One of its roles is to pump blood so that the blood can circulate through the body and then receive it after the blood passed the lungs for cleaning. Unfortunately, heart disease is one of the most deadly disease in the world. One of many tools to support heart disease examination is echocardiography. Echocardiography shows the heart’s left ventricular movement so that doctors can see whether the patient is experiencing ischemia or infarction. Sadly, the examination results depend on the doctors’ experience and accuracy. Hence, in this study, a system with the ability to classify human heart conditions based on left ventricle movement are developed. The methods used in the system include optical flow Lucas-Kanade to track heart cavity movement. The features that will be extracted from the process are distance and direction. Distance feature will be calculated using Euclidean distance formula and direction feature will be calculated according to the points’ angle using cosine triangle formula. And at final, after all the feature obtained, the classification will be done using deep learning method. The tracking and feature extraction process is done succesfully. The classification process obtained 71,43% accuracy.