UNLOCKING NEUROLOGICAL MYSTERIES: MACHINE LEARNING APPROACHES to EARLY DETECTION of ALZHEIMER'S DISEASE
Abstract
Dementia is a clinical illness that becomes more common as people get older. It is defined by a decline in cognitive abilities across several domains and eventually impacts everyday functioning. Consequently, this leads to a decline in autonomy, impairment, dependence on assistance, and ultimately, mortality. Alzheimer's disease (AD) is responsible for 50–80% of all occurrences of dementia, and its occurrence increases by a factor of five every five years beyond the age of 65. Given the availability of health data and the decrease in data processing costs, it is now feasible to detect Alzheimer's disease at an early stage. The objective of this study is to classify individuals as either Alzheimer's sufferers or healthy individuals by employing various machine learning techniques. The OASIS-2 dataset, which consists of longitudinal MRI data from both nondemented and demented older adults, was utilized for this study. Given its potential for early detection of Alzheimer's dementia, the study is anticipated to enhance clinical decision support systems pertaining to modifiable risk factors.
Keywords
Machine Learning Dementia Alzheimer Clinical Decision Support Systems Augmented Intelligence
References
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- Bircan, H. (2004). Lojistik regresyon analizi: Tıp verileri üzerine bir uygulama. Kocaeli University Journal of Social Sciences, (8), 185-208.
- Breiman, L. (2001). Random forests. Machine learning, 45, 5-32.
- Buyrukoğlu, S. (2021). Early Detection of Alzheimer’s Disease using Data Mining: Comparison of Ensemble Feature Selection Approaches. Konya Journal of Engineering Sciences, 9(1), 50-61.
- Darby, R. R., Horn, A., Cushman, F., & Fox, M. D. (2018). Lesion network localization of criminal behavior. Proceedings of the National Academy of Sciences, 115(3), 601-606.
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- Vangara, V., Vangara, S. P. & Thirupathur, K. (2020). Opinion mining classification using naive bayes algorithm. International Journal of Innovative Technology and Exploring Engineering (IJITEE), 9(5), 495-498.
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- Yiğit, A. & Işık, Z. (2018). Application of Artificial Neural Networks in Dementia and Alzheimer's Diagnosis. 26th Signal Processing and Communications Applications Conference (SIU) (pp 1-4).
- Yüzgeç, E. & Talo, M. (2023). Alzheimer ve Parkinson Hastalıklarının Derin Öğrenme Teknikleri Kullanılarak Sınıflandırılması. Fırat Üniversitesi Mühendislik Bilimleri Dergisi, 35(2), 473-482.
Abstract
Yaşla birlikte prevalansı artan demans, birden fazla kognitif alanda bozulma ile seyreden ve sonunda günlük yaşamı etkileyen bir klinik sendromdur. Buna bağlı olarak da bağımsızlığın kaybı, engellilik, bakıma ihtiyaç duyma ve ölümle sonuçlanmaktadır. Tüm demans vakalarının %50-80’ini Alzheimer Hastalığı (AH) oluşturmakta, 65 yaşından sonra olguların görülme sıklığı her beş yılda bir ikiye katlanmaktadır. Bu kapsamda Alzheimer hastalığının erken tespiti sağlık verilerinin erişilebilirliği ve veri işleme maliyetlerinin azalmasıyla artık mümkün hale gelmektedir. Çalışmanın amacı farklı yapay öğrenme yöntemleri kullanarak hastaları Alzheimer ve sağlıklı olarak sınıflandırmaktır. Bu amaçla OASIS-2: Longitudinal MRI Data in Nondemented and Demented Older Adults veri seti kullanılmıştır. Çalışmanın Alzheimer demansını erken tespit etme potansiyeli taşıdığı düşünüldüğünden değiştirilebilir risk faktörleri üzerinde klinik karar destek sistemlerine katkıda bulunması beklenmektedir
References
- Aydın, S., Taşyürek, M. & Öztürk, C. (2022). MR Görüntüleri Ön İşlenerek Derin Ağlar ile Alzheimer Hastalık Tespiti. International Conference on Emerging Sources in Science. (pp. 150-156).
- Belgiu, M., & Drăguţ, L. (2016). Random Forest in Remote Sensing: A review of Applications and Future Directions. ISPRS Journal of Photogrammetry and Remote Sensing, 114, 24-31.
- Bircan, H. (2004). Lojistik regresyon analizi: Tıp verileri üzerine bir uygulama. Kocaeli University Journal of Social Sciences, (8), 185-208.
- Breiman, L. (2001). Random forests. Machine learning, 45, 5-32.
- Buyrukoğlu, S. (2021). Early Detection of Alzheimer’s Disease using Data Mining: Comparison of Ensemble Feature Selection Approaches. Konya Journal of Engineering Sciences, 9(1), 50-61.
- Darby, R. R., Horn, A., Cushman, F., & Fox, M. D. (2018). Lesion network localization of criminal behavior. Proceedings of the National Academy of Sciences, 115(3), 601-606.
- Guerrero-Cristancho, J. S., Vásquez-Correa, J. C. ve Orozco-Arroyave, J. R. (2020). Word-Embeddings and Grammar Features to Detect Language Disorders in Alzheimer’s Disease Patients. TecnoLógicas, 23(47), 63-75.
- Hemrungrojn, S., Tangwongchai, S., Charoenboon, T., Panasawat, M., Supasitthumrong, T., Chaipresertsud, P., Maleevach, P., Likitjaroen, Y., Phanthumchinda, K.ve Maes, M. (2021). Use of the Montreal Cognitive Assessment Thai Version to Discriminate Amnestic Mild Cognitive Impairment from Alzheimer’s Disease and Healthy Controls: Machine Learning Results. Dementia and Geriatric Cognitive Disorders, 50(2), 183-194.
- Işık, A. T. (2009). Alzheimer Hastalığı. A. T. Işık ve O. Tanrıdağ (Eds.), In Geriatri Pratiğinde Demans Sendromu (pp. 90-122). İstanbul: Som Kitap.
- Karabay, G. S. & Çavaş, M. (2022). Derin Öğrenme Yöntemiyle Alzheimer Hastalığının Tespiti. Fırat Üniversitesi Mühendislik Bilimleri Dergisi, 34(2), 879-887.
- Ke, G., Meng, Q., Finley, T., Wang, T., Chen, W., Ma, W., ... & Liu, T. Y. (2017). LightGBM: A highly efficient gradient boosting decision tree. Advances in neural information processing systems, 30.
- Luz, S., De La Fuente Garcia, S. & Albert, P. (2018). A Method for Analysis of Patient Speech in Dialogue for Dementia Detection. Resources and Processing of linguistic, para-linguistic and extra-linguistic Data from people with various forms of cognitive impairment. (pp. 35-42). ELRA. Paris.
- Marcus, D. S., Fotenos, A. F., Csernansky, J. G., Morris, J. C., & Buckner, R. L. (2010). Open access series of imaging studies: longitudinal MRI data in nondemented and demented older adults. Journal of cognitive neuroscience, 22(12), 2677-2684.
- Pan, Y., Mirheidari, B., Reuber, M., Venneri, A., Blackburn, D. ve Christensen, H. (2020). Improving Detection of Alzheimer’s Disease using Automatic Speech Recognition to Identify High-Quality Segments for More Robust Feature Extraction. In Proceedings of Interspeech 2020 (pp. 4961-4965).
- Petersen, R. C. (2007). Mild cognitive impairment. Continuum: Lifelong Learning in Neurology, 13(2), 15-38.
- Pope, C. & Davis, B. H. (2011). Finding a Balance: The Carolinas Conversation Collection. Corpus Linguistics and Linguistic Theory, 7(1):143–161.
- Prent, N., Jonker, F. A., Schouws, S. N., & Jonker, C. (2023). The risk of criminal behavior in the elderly and patients with neurodegenerative disease. Handbook of Clinical Neurology, 197, 181-196.
- Ramzan, F., Khan, M. U. G., Rehmat, A., Iqbal, S., Saba, T., Rehman, A. & Mehmood, Z. (2020). A Deep Learning Approach for Automated Diagnosis and Multi-Class Classification of Alzheimer’s Disease Stages using Resting-State FMRI and Residual Neural Networks. Journal of Medical Systems, 44, 1-16.
- Sertkaya, M. E. & Ergen, B. (2022). Alzheimer Hastalığının Erken Teşhisinin Çoklu Değişken Kullanarak Tespiti. Avrupa Bilim ve Teknoloji Dergisi, (35), 306-314.
- Subramoniam, M., Aparna T. R., Anurenjan, P. R. & Sreeni, K. G. (2022). Deep Learning-Based Prediction of Alzheimer’s Disease from Magnetic Resonance Images. M. Saraswat, H. Sharma ve K. V. Arya (Eds.) In Intelligent Vision in Healthcare içinde (pp. 145-151). Springer Publishing.
- Turkish Statistical Institute (2022). İstatistiklerle Yaşlılar, 2022. https://data.tuik.gov.tr/Bulten/Index?p=%C4%B0statistiklerle-Ya%C5%9Fl%C4%B1lar-2022-49667&dil=1. (16.02.2024).
- Vangara, V., Vangara, S. P. & Thirupathur, K. (2020). Opinion mining classification using naive bayes algorithm. International Journal of Innovative Technology and Exploring Engineering (IJITEE), 9(5), 495-498.
- World Health Organization (2023). Dementia Key Facts. https://www.who.int/news-room/fact-sheets/detail/dementia (16.02.2024).
- Yiğit, A. & Işık, Z. (2018). Application of Artificial Neural Networks in Dementia and Alzheimer's Diagnosis. 26th Signal Processing and Communications Applications Conference (SIU) (pp 1-4).
- Yüzgeç, E. & Talo, M. (2023). Alzheimer ve Parkinson Hastalıklarının Derin Öğrenme Teknikleri Kullanılarak Sınıflandırılması. Fırat Üniversitesi Mühendislik Bilimleri Dergisi, 35(2), 473-482.
Details
| Primary Language | English |
|---|---|
| Subjects | Applied Computing (Other) |
| Journal Section | Articles |
| Authors | |
| Publication Date | May 29, 2024 |
| Submission Date | February 17, 2024 |
| Acceptance Date | May 15, 2024 |
| Published in Issue | Year 2024 Volume: 13 Issue: 1 |
The open access articles in the journal are licensed under the terms of the Creative Commons Attribution-NonCommercial-NoDerivs 4.0 International (CC BY-NC-ND 4.0) license.