Application of Artificial Neural Network Method as A Detection of Blood Fat Disorders in Images of Complete Blood Examination

https://doi.org/10.20473/iapl.v2i2.31510
Artificial Neural Networks Backpropagation Blood Fat Abnormalities Coronary Heart Disease Detection

Authors

  • Catharina Natasa Bella Fortuna Biomedical Engineering Study Program, Department of Physics, Faculty of Science and Technology, Universitas Airlangga, Indonesia
  • Franky Chandra Satria Arisgraha, S.T., M.T.
    franky-c-s-a@fst.unair.ac.id
    Biomedical Engineering Study Program, Department of Physics, Faculty of Science and Technology, Universitas Airlangga, Indonesia
  • Puspa Erawati Biomedical Engineering Study Program, Department of Physics, Faculty of Science and Technology, Universitas Airlangga, Indonesia
Vol. 2 No. 2 (2021): December
Articles
December 2, 2021

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Based on various epidemiological studies, it is stated that blood lipids are the main risk factor for atherosclerosis that leads to coronary heart disease. In patients with blood lipid disorders, red blood cells undergo deformability so that their shape is flatter than normal red blood cells, which are round. The research entitled Application of Artificial Neural Network Method as Detection of Blood Fat Abnormalities in Image of Complete Blood Examination Results was conducted to help facilitate laboratory examinations. This research hopes that it will provide appropriate early detection to support the expert diagnosis. This research consists of two stages. The first stage is digital image processing to obtain area, perimeter, and eccentricity features. These three features will be used as input to the Backpropagation Neural Network program as the second stage. At this stage, blood lipid abnormalities are detected from features that have been obtained from image processing. The accuracy of detecting blood lipid abnormalities with ANN Backpropagation is 85%.

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