ADALINE Neural Network For Early Detection Of Cervical Cancer Based On Behavior Determinant
(1) Tanjungpura University
(2) Tanjungpura University
(3) Indonesian University of Education
Abstract
Purpose: Cervical cancer is one of the most common types of cancer that kills women worldwide. One way for early detection of cervical cancer risk is by looking at human behavior determinants. Detection of cervical cancer based on behavior determinants has been researched before using Naïve Bayes and Logistic Regression but has never using ADALINE Neural Network. Methods: In this paper, ADALINE proposes to detect early cervical cancer based on the behavior on the UCI dataset. The data used are 72 data, consisting of 21 cervical cancer patients and 51 non-cervical cancer patients. The dataset is divided 70% for training data and 30% for testing data. The learning parameters used are maximum epoch, learning rate, and MSE. Result: MSE generated from ADALINE training process is 0.02 using a learning rate of 0.006 with a maximum epoch of 19. Twenty-two test data obtained an accuracy of 95.5%, and overall data got an accuracy value of 97.2%. Novelty: One alternative method for early detection of cervical cancer based on behavior is ADALINE Neural Network.
Keywords
Full Text:
PDFReferences
E. Nurlelawati, T. E. R. Devi, and I. Sumiati, “Faktor-faktor yang berhubungan dengan kejadian kanker serviks di rumah sakit pusat pertamina jakarta tahun 2016,” J. Midwife, vol. 5, no. 01, pp. 8–16, 2018.
C. A. Fitrisia, D. Khambri, B. I. Utama, and S. Muhammad, “Analisis faktor-faktor yang berhubungan dengan kejadian lesi pra kanker serviks pada wanita pasangan usia subur di wilayah kerja puskesmas muara bungo 1,” J. Kesehatan. Andalas, vol. 8, no. 4, pp. 33–43, 2019.
Sobar, R. Machmud, and A. Wijaya, “Behavior determinant based cervical cancer early detection with machine learning algorithm,” Adv. Sci. Lett., vol. 22, no. 10, pp. 3120–3123, 2016.
P. C. Siswipraptini, R. N. Aziza, I. Sangadji, and I. Indrianto, “The design of a smart home controller based on ADALINE,” TELKOMNIKA Indones. J. Electr. Eng., vol. 18, no. 4, pp. 2177–2185, 2020.
J. Viloria-Porto, C. Robles-Algarín, and D. Restrepo-Leal, “Code and data from an ADALINE network trained with the RTRL and LMS algorithms for an MPPT controller in a photovoltaic system,” Data in Brief, vol. 32, p. 106296, 2020.
R. Irsyada and H. Ausytra, “Penerapan Jaringan syaraf tiruan model ADALINE pada klasifikasi status gizi balita (berdasarkan metode antropometri),” J. INVERTOR, vol. 2, no. 1, pp. 13–18, 2019.
W. Ge, B. Wang, and H. Mu, “Dynamic parameter identification for reconfigurable robot usingADALINE neural network,” in Proc. 2019 IEEE Int. Conf. Mechatron. Autom. (ICMA), 2019, pp. 319–324.
B. Etikasari and T. D. Pusputasari, “Pengenalan pola huruf dengan menggunakan algoritma ADALINE,” J. MNEMONIC., vol. 2, no. 1, pp. 12–16, 2019.
M. Sujith and S. Padma, “Optimization of harmonics with active power filter based on ADALINE neural network,” Microprocess. Microsyst., vol. 73, p. 102976, 2020.
R. Kahankova, R. Martinek, M. Mikolášová and R. Jaroš, “Adaptive linear neuron for fetal electrocardiogram extraction,” in 2018 IEEE 20th Int. Conf. e-Health Netw. Appl. Serv. (Heal.), 2018, pp. 1–5.
M. Takrouri and R. Dhaouadi, “ADALINE-based friction identification and compensation of a linear voice-coil DC motor,” in 2018 5th Int. Conf. Electr. Power Energy Convers. Syst. (EPECS), 2018, pp. 1–6.
A. S. Eesa and W. K. Arabo, “Normalization methods for backpropagation: a comparative study,” Sci. J. Univ. Zakho, vol. 5, no. 4, pp. 319–323, 2017.
S. N. Sivanandam, S. Sumathi, and S. N. Deepa, Introduction to neural networks using MATLAB 6.0. Tata McGraw-Hill Publishing Company Limited, 2006.
U. M. Tukur and S. M. Shamsuddin, “Radial basis function network learning with modified backpropagation algorithm,” TELKOMNIKA Indones. Jo urnal Electr. Eng., vol. 13, no. 2, pp. 369–378, 2015.
S. N. Subhashree, S. Sunoj, O. Hassanijalilian, and C. Igathinathane, “Decoding common machine learning methods : agricultural application case studies using open source software,” in Appl. Intell.Decis. Mak. Mach. Learn., First edit., Florida: CRC Press, 2021, pp. 21–52.
Refbacks
- There are currently no refbacks.
Scientific Journal of Informatics (SJI)
p-ISSN 2407-7658 | e-ISSN 2460-0040
Published By Department of Computer Science Universitas Negeri Semarang
Website: https://journal.unnes.ac.id/nju/index.php/sji
Email: [email protected]
This work is licensed under a Creative Commons Attribution 4.0 International License.