DOI: https://doi.org/10.14710/jtsiskom.8.1.2020.6-11

Identification of fat-soluble vitamins deficiency using artificial neural network

Department of Information System, Faculty of Computer Science and Engineering, Krida Wacana Christian University, Indonesia

Received: 29 Apr 2019; Revised: 14 Oct 2019; Accepted: 17 Oct 2019; Available online: 5 Nov 2019; Published: 31 Jan 2020.
Open Access Copyright (c) 2020 Jurnal Teknologi dan Sistem Komputer
Creative Commons License This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.

Citation Format:
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.
Fulltext View|Download Email colleagues
Keywords: deficiency early diagnose; fat-soluble vitamin deficiency; neural network; back-propagation
Funding: Krida Wacana Christian University

Article Metrics:

Article Info
Section: Original Research Articles
Language : EN
Most viewed articles
Perancangan Sistem Informasi Rekam Medis Rawat Inap Rumah Sakit Berbasis Web Pembuatan Aplikasi Antar-Jemput Laundry Berbasis Web Service pada Platform Android K-means-SMOTE for handling class imbalance in the classification of diabetes with C4.5, SVM, and naive Bayes Data Mining using Apriori Algorithm for Product Recommendation for Customers Optimization for prediction model of palm oil land suitability using spatial decision tree algorithm More articles
Most cited articles
Intelligent Robot for Measuring Consumer Satisfaction at Shopping Center Pengembangan Sistem Informasi Rekam Medis untuk Dinas Kabupaten Grobogan Parameter tuning in KNN for software defect prediction: an empirical analysis Sistem Pengendali Peralatan Elektronik dalam Rumah secara Otomatis Menggunakan Sensor PIR, Sensor LM35, dan Sensor LDR Web Application of Lecturer Performance Assesment on Teaching Process: A Case Study at Internal Quality Assurance Agency of Institut Teknologi Padang More cited articles
  1. M. Khosravi-Largani et al.,“A review on potential roles of vitamins in incidence, progression, and improvement of multiple sclerosis,” eNeurologicalSci, vol. 10, pp. 37-44, 2018. doi: 10.1016/j.ensci.2018.01.007
  2. M. S. Almetwazi, A. O. Noor, D. M. Almasri, I. Popovici, T. Alhawassi, K. A. Alburikan, and C. A. Harrington, “The association of vitamin d deficiency and glucose control among diabetic patients,” Saudi Pharmaceutical Journal, vol. 25, no. 8, pp. 1179-1183, 2017. doi: 10.1016/j.jsps.2017.09.001
  3. P. Ravisankar, A. A. Reddy, B. Nagalakshmi, O. S. Koushik, B. V. Kumar, and P. S. Anvith, “The comprehensive review on fat soluble vitamins,” IOSR Journal of Pharmacy, vol.5, no. 11, pp. 12-28, 2015
  4. A. A. Albahrani and R. F. Greaves, “Fat-soluble vitamins: clinical indications and current challenges for chromatographic measurement,” The Clinical biochemistry Reviews, vol. 37, no. 1, pp. 27-47, 2016
  5. National Research Council (US) Subcommittee on the Tenth Edition of the Recommended Dietary Allowances, Recommended Dietary Allowances: 10th Edition. Washington (DC): National Academies Press (US), 1989
  6. A. Labellapansa and A. T. Boyz, “Sistem pakar diagnosa dini defisiensi vitamin dan mineral,” Jurnal Informatika, vol. 10, no. 1, pp. 1156–1163, 2016
  7. N. Sevani and M. Joshua, “Implementasi forward chaining untuk diagnosa defisiensi vitamin larut dalam lemak berbasiskan web,” Jurnal Teknologi Komputer dan Informatika, vol. 10, no. 2, pp. 51– 59, 2014. doi: 10.21460/inf.2014.102.293
  8. F. Sayfria, B. Iqbal, E. Budianita, and I. Afrianty, “Implementation of backpropagation neural network to detect suspected lung disease,” Indonesian Journal of Artificial Intelligence and Data Mining (IJAIDM), vol.1, no.1, pp.32-40, 2018. doi: 10.24014/ijaidm.v1i1.5023
  9. H. H. Örkcü and H. Bal, "Comparing performances of backpropagation and genetic algorithms in the data classification," Expert Systems with Applications, vol.38, no.4, pp. 3703–3709, 2011. doi: 10.1016/j.eswa.2010.09.028
  10. I. Y. Khan, P. H. Zope, and S.R. Suralkar, “Importance of artificial neural network in medical diagnosis disease like acute nephritis disease and heart disease,” International Journal of Engineering Science and Innovative Technology (IJESIT), vol. 2, vo.2, pp. 210-217, 2013
  11. I. A. Basheer and M. Hajmeer, "Artificial neural networks: fundamentals, computing, design, and application," Journal of Microbiological Methods, vol. 43, no.1, pp. 3-31, Dec 2000. doi: 10.1016/S0167-7012(00)00201-3
  12. F. Novadiwanti, A. Buono, and A. Faqih, “Prediksi awal musim hujan di kabupaten Pacitan menggunakan optimasi cascade neural network (CNN) dengan genetic algorithm (GA) berdasarkan data GCM,” Jurnal Tanah dan Iklim, vol. 41, no. 1, pp. 69-77, 2017

Last update:

No citation recorded.

Last update: 2024-04-17 04:21:51

No citation recorded.