Epoch in a neural network for brain stroke
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Published:
Mar 30, 2023
Keywords:
Accuracy, Artificial Neural Networks, Epoch, Strokes
Section
Computer Science
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Article View : 175 Times
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Abstract
A neural network is a data processing system consisting of a large number of simple and highly interconnected processing elements in an architecture inspired by the structure of the cortical regions of the brain. Therefore, neural networks can often do things that humans or animals can do, but traditional computers are often lousy. This research discusses brain tumors that can be detected by artificial intelligence. Stroke includes the sudden death of brain cells due to lack of oxygen, blockage of the circulatory system, or severance of flexible pathways to the brain. Therefore the need for action that must be faster to be able to detect this deadly disease. The method used is a Neural Network which can collect knowledge by detecting patterns and relationships between data and learning experiences. So that the detection process is carried out more quickly and the patient can be given medical action as soon as possible. In the study I conducted brain stroke from the number of strokes with a value of 0 4733 and 1 out of 248. This research has a test conducted by conducting epoch training from 1 to 300, the highest score accuracy is in epoch 1 and 2 with more high scores.
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How to Cite
Simanjuntak, M., Simangunsong, J., & Hongjie Dai. (2023). Epoch in a neural network for brain stroke. International Journal of Basic and Applied Science, 11(4), 172–179. https://doi.org/10.35335/ijobas.v11i4.156
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“Training-time-of-one-epoch-with-different-step-hours.”
D. D. Gnonlonfoun et al., “Post-Stroke Cognitive Disorders and Associated Factors in French Speaking West Africa, Benin Case,” Neurosci. Med., vol. 05, no. 01, pp. 32–41, 2014, doi: 10.4236/nm.2014.51006.
D. Darwish, “Simulation and Evaluation of Signature Recognition Techniques,” vol. 37, no. 3, pp. 65–74, 2013.
N. Akhtar et al., “Stroke Mimics: A five-year follow-up study from the Qatar Stroke Database,” J. Stroke Cerebrovasc. Dis., vol. 29, no. 10, p. 105110, 2020, doi: https://doi.org/10.1016/j.jstrokecerebrovasdis.2020.105110.
M. Frotscher, Diagnosis Topik Neurologi Duus (Anatomi, Fisiologi, Tanda, Gejala), Ed.4. Jakarta : EGC., 2010. [Online]. Available: https://perpustakaan.rsmoewardi.com/index.php?p=show_detail&id=219
M. S. Simanjuntak, “Jurnal Mantik Jurnal Mantik,” Act. Act. Funct. Multilayer Perceptron - Based Card. Abnorm., vol. 3, no. 2, pp. 10–19, 2019, [Online]. Available: http://iocscience.org/ejournal/index.php/mantik/article/view/747/530
H. Dai et al., “Cancer Registry Coding via Hybrid Neural Symbolic Systems in the Cross-Hospital Setting,” IEEE Access, vol. PP, p. 1, Jul. 2021, doi: 10.1109/ACCESS.2021.3099175.
W. R. Isna and F. Abdullah, “Closed Kinetic Chain Exerciseefektif Dalam Meningkatkan Kemampuan Fungsional Pada Osteoartritis Lutut,” J. Ilm. Fisioter., vol. 3, no. 2, pp. 1–7, 2020, doi: 10.36341/jif.v3i2.1379.
J. Adnan et al., “Heart abnormality activity detection using multilayer perceptron (MLP) network,” AIP Conf. Proc., vol. 2016, no. September, 2018, doi: 10.1063/1.5055415.
L. Bartholomé and Y. Winter, “Quality of Life and Resilience of Patients With Juvenile Stroke: A Systematic Review.,” J. stroke Cerebrovasc. Dis. Off. J. Natl. Stroke Assoc., vol. 29, no. 10, p. 105129, Oct. 2020, doi: 10.1016/j.jstrokecerebrovasdis.2020.105129.
I. Iskandar, A. Hadi, and A. Alfridsyah, “Faktor Risiko Terjadinya Penyakit Jantung Koroner pada Pasien Rumah Sakit Umum Meuraxa Banda Aceh,” AcTion Aceh Nutr. J., vol. 2, no. 1, p. 32, 2017, doi: 10.30867/action.v2i1.34.
V. Abedi et al., “Novel Screening Tool for Stroke Using Artificial Neural Network,” Stroke, vol. 48, no. 6, pp. 1678–1681, 2017, doi: 10.1161/STROKEAHA.117.017033.
G. C. O’Connell et al., “Use of deep artificial neural networks to identify stroke during triage via subtle changes in circulating cell counts.,” BMC Neurol., vol. 22, no. 1, p. 206, Jun. 2022, doi: 10.1186/s12883-022-02726-x.
B. Rifai, “Algoritma Neural Network Untuk Prediksi,” Techno Nusa Mandiri, vol. IX, no. 1, pp. 1–9, 2013.
M. Mijwil, A.-H. Al-Mistarehi, and D. S. Mutar, “The Practices of Artificial Intelligence Techniques and Their Worth in the Confrontation of COVID-19 Pandemic: A Literature Review,” Mob. Forensics, vol. 4, no. 1, pp. 11–30, 2022, doi: 10.12928/mf.v4i1.5691
C. Dewi, D. T. P. Yanto, and H. Hastuti, “the Development of Power Electronics Training Kits for Electrical Engineering Students: a Validity Test Analysis,” J. Pendidik. Teknol. Kejuru., vol. 3, no. 2, pp. 114–120, 2020, doi: 10.24036/jptk.v3i2.9423.
Y. Q. Lee et al., “Protected Health Information Recognition of Unstructured Code-Mixed Electronic Health Records in Taiwan,” Stud. Health Technol. Inform., vol. 290, pp. 627–631, 2022, doi: 10.3233/SHTI220153.
M. S. Sirsat, E. Fermé, and J. Câmara, “Machine Learning for Brain Stroke: A Review,” J. Stroke Cerebrovasc. Dis., vol. 29, no. 10, 2020, doi: 10.1016/j.jstrokecerebrovasdis.2020.105162.
M.-P. Hosseini et al., “Review of Machine Learning Algorithms for Brain Stroke Diagnosis and Prognosis by EEG Analysis,” 2020, [Online]. Available: http://arxiv.org/abs/2008.08118
W.-S. Cheng, G.-Y. Chen, X.-Y. Shih, M. Elsisi, M.-H. Tsai, and H.-J. Dai, “Vickers Hardness Value Test via Multi-Task Learning Convolutional Neural Networks and Image Augmentation,” Appl. Sci., vol. 12, no. 21, p. 10820, 2022, doi: 10.3390/app122110820.
F. Izhari, M. Zarlis, and Sutarman, “Analysis of backpropagation neural neural network algorithm on student ability based cognitive aspects,” IOP Conf. Ser. Mater. Sci. Eng., vol. 725, no. 1, 2020, doi: 10.1088/1757-899X/725/1/012103.
D. G. S. & D. N. S. [1]. D. Shanthi, “Designing an Artificial Neural Network Model for the Prediction of Thrombo-embolic Stroke,” Dep. Comput. Sci. Eng. Birla Inst. Technol., 2020.
A. Tahmasebi and H. Pourghassem, “Signature identification using dynamic and HMM features and KNN classifier,” Proc. - 2013 Int. Conf. Commun. Syst. Netw. Technol. CSNT 2013, pp. 201–205, 2013, doi: 10.1109/CSNT.2013.51.
E. [2]. Feng, R., Badgeley , M., Mocco , J., & Oermann, “Deep learning guided stroke management: a review of clinical applications,” J. neurointerventional surgery., 2020.
Andy Hidayat Jatmika, S. P. Waskito, and Ariyan Zubaidi, “Implementasi Algoritma Pemilihan Node Tetangga Terbaik Pada Protokol Routing DSR di Jaringan MANET,” J. Comput. Sci. Informatics Eng., vol. 6, no. 1, pp. 64–72, 2022, doi: 10.29303/jcosine.v6i1.435.
“Training-time-of-one-epoch-with-different-step-hours.”
D. D. Gnonlonfoun et al., “Post-Stroke Cognitive Disorders and Associated Factors in French Speaking West Africa, Benin Case,” Neurosci. Med., vol. 05, no. 01, pp. 32–41, 2014, doi: 10.4236/nm.2014.51006.
D. Darwish, “Simulation and Evaluation of Signature Recognition Techniques,” vol. 37, no. 3, pp. 65–74, 2013.
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