Identification of fat-soluble vitamins deficiency using artificial neural network
| Autor: | Frahselia Tandipuang, Noviyanti Sagala, Cynthia Hayat |
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| Rok vydání: | 2019 |
| Předmět: |
Training set
Artificial neural network neural network business.industry Activation function 020206 networking & telecommunications Pattern recognition QA75.5-76.95 02 engineering and technology deficiency early diagnose Backpropagation Identification (information) Fat-Soluble Vitamin fat-soluble vitamin deficiency Electronic computers. Computer science 0202 electrical engineering electronic engineering information engineering Medicine 020201 artificial intelligence & image processing Artificial intelligence business Gradient descent back-propagation Test data |
| Zdroj: | Jurnal Teknologi dan Sistem Komputer, Vol 8, Iss 1, Pp 6-11 (2020) |
| ISSN: | 2338-0403 2620-4002 |
| DOI: | 10.14710/jtsiskom.8.1.2020.6-11 |
| Popis: | The fat-soluble vitamins (A, D, E, K) deficiency remain frequent universally and may have consequential adverse resultants and causing slow appearance symptoms gradually and intensify over time. The vitamin deficiency detection requires an experienced physician to notice the symptoms and to review a blood test’s result (high-priced). This research aims to create an early detection system of fat-soluble vitamin deficiency using artificial neural network Back-propagation. The method was implemented by converting deficiency symptoms data into training data to be used to produce a weight of ANN and testing data. We employed Gradient Descent and Logsig as an activation function. The distribution of training data and test data was 71 and 30, respectively. The best architecture generated an accuracy of 95 % in a combination of parameters using 150 hidden layers, 10000 epoch, error target 0.0001, learning rate 0.25. |
| Databáze: | OpenAIRE |
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