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Thailand has entered an aging society as a result of its population's longevity; in 2020, there were 11.627 million persons in Thailand who were 60 or older, or 17.57 percent of the country's overall population. By 2030, Thailand will have a population that is 60 years of age or older, making up approximately 28% of the total population. Everyone's body naturally deteriorates with age, and dementia, particularly Alzheimer's disease, is one of the more common conditions. The number of individuals who have this disease increases with age, increasing by around double every five years. Both treatment and prevention are not possible for this illness. Early detection of Alzheimer's disease increases the likelihood that symptoms can be treated to improve or delay further decline. In this study, simple data preparation techniques and magnetic resonance imaging (MRI) data were used to categorize Alzheimer's disease using deep neural network (DNN). The 5,121 total images used in this study were composed of 2,560 MRI images for the normal case and 2,561 MRI images for the Alzheimer case. According to the data analysis, this model has an accuracy of 97.56%, a precision of 98.22%, a recall of 96.89%, and an F1Score of 97.54%.
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S. Trakulsithichoke, “Prevention of dementia in older persons,” (in Thai), J. Nursing and Health Care, vol. 36 no. 4, pp. 6–14, 2018.
S. Mangmee and S. Monkong, “Relationships between mutuality, preparedness, and predictability to care, and caregiver role strain from caregiving activities for older people with dementia,” (in Thai), J. Thailand Nursing and Midwifery Council, vol. 36, no. 3, pp. 151–164, 2021.
S. Pornudomthap, C. Somthawinpongsai, and P. Piphitpakdee, “The application development of dementia / Alzheimer patients in daily life,” (in Thai), J. Manage. Sci., Ubon Ratchathani Univ., vol. 11 no. 1, pp. 60–85, 2022.
L. Inklab, “Understanding & approaching the Alzheimer patients,” (in Thai), Christian Univ. Thailand J., vol. 20, no. 3, pp. 439–447, 2014.
W. Kongin, O. Thammson, S. Thipsrinuan, and S. Kaewborrisuth, “How can we help an Alzheimer’s patient and caregivers?,” (in Thai), The Thai J. Nursing Council, vol. 15 no. 3, pp. 65–77, 2001.
P. Anundilokrit, “Dementia,” (in Thai), Regional Health Promotion Center 9 J., vol. 15, no. 37, pp.392–398, 2021.
M. Prince, M. Guerchet, and M. Prina, “Policy brief for heads of government: The global impact of dementia 2013–2050,” Alzheimer's Disease International (ADI), London, U.K., Dec. 1, 2013, [Online]. Available: https://www.alzint.org/u/2020/08/GlobalImpactDementia2013.pdf
P. Prasartkul and N. Satchanawakul, “The essential of ageing in place in Thailand,” (in Thai), Thammasat Review, vol. 40 no. 2, pp. 1-23, 2021.
C. Kavitha, V. Mani, S. R. Srividhya, O. I. Khalaf, and C. A. Tavera Romero, “Early-stage Alzheimer’s disease prediction using machine learning models,” Frontiers in Public Health, vol. 10, Mar. 2022, Art. no. 853294.
C. Pamarapa, T. Ekjeen, W. Shoombuatong, and Y. Vichianin, “Alzheimer’s disease classification and prediction using T1- weighted MR brain imaging based on SVM algorithm,” The Thai J. Radiological Technol., vol. 46 no. 1, pp. 69–79, 2021.
I. Beheshti, H. Demirel, and H. Matsuda, “Classification of Alzheimer’s disease and prediction of mild cognitive impairment-to-Alzheimer’s conversion from structural magnetic resource imaging using feature ranking and a genetic algorithm,” The Thai J. Radiological Technol., vol. 83, pp. 109–119, Apr. 2017.
A. Farooq, S. Anwar, M. Awais, and S. Rehman, “A deep CNN based multi-class classification of Alzheimer's disease using MRI,” in Proc. IEEE Int. Conf. Imaging Syst. and Techniques (IST), Beijing, China, Oct. 2017, pp. 1–6.
S. Liu et al. “Generalizable deep learning model for early Alzheimer’s disease detection from structural MRIs,” Sci. Rep., vol. 12, pp. 17106–17118, Oct. 2022.
S. S. Kundaram and K. C. Pathak, “Deep learning-based Alzheimer disease detection,” in Proc. 4th Int. Conf. Microelectronics, Comput. and Commun. Syst., Ranchi, India, May 2019, pp 587–597.
R. Maskeliunas, R. Damasevicius, and T. Krilavicius, “Analysis of features of Alzheimer’s disease: detection of early stage from functional brain changes in magnetic resonance images using a finetuned ResNet18 network,” Diagnostics, vol. 11, pp. 1071–1087, Jun. 2021.
E. Mggdadi, A. Al-Aiad, M. S. Al-Ayyad, and A. Darabseh, “Prediction Alzheimer's disease from MRI images using deep learning,” in Proc. 12th Int. Conf. Inform. and Commun. Syst. (ICICS), Valencia, Spain, May 2021, pp. 120–125.
J. Heaton. “The number of hidden layers.” HEATONRESEARCH.com. https://www.heatonresearch.com/2017/06/01/hidden-layers.html (accessed Jan. 19, 2023).
S. Srithongchai, “A PM2.5 prediction model using LSTM neural network in Bangkok area,” J. Eng. and Digital Technol. (JEDT), vol. 10, no. 1, pp. 1–9, 2022.
Y. Bengio, “Practical Recommendations for Gradient-Based Training of Deep Architectures,” in Neural Networks: Tricks of the Trade, G. Montavon, G. B. Orr, and K.-R. Müller, Eds. Berlin, Germany: Springer, 2012, pp. 437–478.