Identification of fat-soluble vitamins deficiency using artificial neural network

N. Sagala, C. Hayat, Frahselia Tandipuang
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引用次数: 0

Abstract

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.
脂溶性维生素缺乏症的人工神经网络鉴定
脂溶性维生素(A、D、E、K)缺乏症仍然普遍存在,并可能产生相应的不良后果,导致症状逐渐出现并随着时间的推移而加剧。维生素缺乏检测需要有经验的医生注意症状并审查血液检测结果(价格高昂)。本研究旨在利用人工神经网络反向传播建立脂溶性维生素缺乏症的早期检测系统。该方法是通过将缺陷症状数据转换为训练数据来实现的,训练数据用于产生ANN和测试数据的权重。我们采用梯度下降和Logsig作为激活函数。训练数据和测试数据的分布分别为71和30。最佳架构在使用150个隐藏层、10000个历元、错误目标0.0001、学习率0.25的参数组合中产生了95%的准确率。
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