Alzheimer Hastalığının Tespitinde Makine Öğrenmesi Algoritmalarının Karşılaştırılması

Öz

Alzheimer hastalığı, bireylerde bilişsel fonksiyon kaybı ve bilişsel gerilemeye neden olan nörodejeneratif bir rahatsızlıktır. Hastalığın erken evrede tespit edilmesi hastalığın yıkıcı etkilerini yavaşlatmak için önem arz etmektedir. Uzman doktorlara teşhis sürecinde yardımcı olabilecek otonom bir bilgisayarlı bir destek sisteminin kullanılması zamandan tasarruf sağlayarak insan hatasının azaltılmasına yardımcı olur. Bu nedenle, Alzheimer hastalığının erken teşhisi için makine öğrenmesi algoritmalarından yararlanılarak yüksek doğruluklu bir sınıflandırma çalışması hedeflenmiştir. Bu çalışma kapsamında, 24 adet sağlıklı ve 24 adet Alzheimer hastası gönüllüden alınan Elektroensefalogram (EEG) sinyalleri ile oluşturulmuş açık kaynak olarak sunulan bir veri setinden yararlanılmıştır. EEG sinyallerinin her bir kanalından spektral ve istatistiksel öznitelikler olmak üzere 28 öznitelik çıkartılmıştır. Çıkartılan öznitelikler, karar ağacı öznitelik önem algoritmasına uygulanmış ve Alzheimer bireyler ile sağlıklı bireyleri ayırt edebilecek en anlamlı 5 öznitelik belirlenmiştir. Belirlenen öznitelikler ile dört makine öğrenmesi algoritması eğitilmiştir. Eğitim için verilerin %70’i kullanılmış ve algoritmalar 10-kat çapraz doğrulama yöntemi ile eğitilmiştir. Daha önce algoritmaların görmediği, test için ayrılan veriler ile makine öğrenmesi algoritmaları test edildiğinde en yüksek doğruluk % 96.43 ile Gradient Boosting Sınıflandırıcısı (GBC) algoritması ile elde edilmiştir.

Anahtar Kelimeler

• Alzheimer
• Öznitelik çıkarma
• Öznitelik seçme
• Gradyan artırmalı sınıflandırma
• makine öğrenmesi

Comparison Of Machine Learning Algorithms In The Detection Of Alzheimer's Disease

Abstract

Alzheimer's disease is a neurodegenerative disorder that causes loss of cognitive function and cognitive decline in individuals. Detection of the disease at an early stage is important to slow down the devastating effects of the disease. The use of an autonomous computerized support system that can assist specialist physicians in the diagnostic process saves time and helps reduce human error. For this reason, a high-accuracy classification study was aimed at utilizing different machine learning algorithms for early diagnosis of Alzheimer's disease. Within the scope of this study, an open source data set created with Electroencephalogram (EEG) signals from 24 healthy and 24 Alzheimer's patient volunteers was used. 28 features, including spectral and statistical features, were extracted from each channel of the EEG signals. The extracted features were evaluated to the feature importance algorithm and the five most significant features that could distinguish between Alzheimer's individuals and healthy individuals were determined. Four machine learning algorithms are trained with the determined features. 70% of the data was used for training and the algorithms were trained with a 10-fold cross-validation method. When the four machine learning algorithms were tested with the data reserved for testing, which the algorithms had not seen before, the highest accuracy was obtained with the Gradient Boosting Classifier (GBC) algorithm with 96.43%.

Keywords

• alzheimer
• EEG
• Feature Extraction
• Gradient Boosing Classifier

References

1. Ahmadlou, M., Adeli, H., & Adeli, A. (2011). Fractality and a wavelet-chaos-methodology for EEG-based diagnosis of Alzheimer disease. Alzheimer Disease & Associated Disorders, 25(1), 85–92.
2. Al Iqbal, M. D. R., Rahman, S., Nabil, S. I., & Chowdhury, I. U. A. (2012). Knowledge based decision tree construction with feature importance domain knowledge. 2012 7th International Conference on Electrical and Computer Engineering, 659–662.
3. Babiloni, C., Lizio, R., Marzano, N., Capotosto, P., Soricelli, A., Triggiani, A. I., Cordone, S., Gesualdo, L., & Del Percio, C. (2016). Brain neural synchronization and functional coupling in Alzheimer’s disease as revealed by resting state EEG rhythms. International Journal of Psychophysiology, 103, 88–102.
4. Bairagi, V. (2018). EEG signal analysis for early diagnosis of Alzheimer disease using spectral and wavelet based features. International Journal of Information Technology, 10(3), 403–412.
5. Bauer, E., & Kohavi, R. (1999). An empirical comparison of voting classification algorithms: Bagging, boosting, and variants. Machine Learning, 36(1), 105–139.
6. Benz, N., Hatz, F., Bousleiman, H., Ehrensperger, M. M., Gschwandtner, U., Hardmeier, M., Ruegg, S., Schindler, C., Zimmermann, R., & Monsch, A. U. (2014). Slowing of EEG background activity in Parkinson’s and Alzheimer’s disease with early cognitive dysfunction. Frontiers in Aging Neuroscience, 6, 314.
7. Breiman, L. (1996). Bagging predictors. Machine Learning, 24(2), 123–140.
8. Chakrabarti, S., Cox, E., Frank, E., Güting, R. H., Han, J., Jiang, X., Kamber, M., Lightstone, S. S., Nadeau, T. P., & Neapolitan, R. E. (2008). Data mining: know it all. Morgan Kaufmann.

9. Cortes, C., & Vapnik, V. (1995). Support-vector networks. Machine Learning, 20(3), 273–297.
10. Cover, T., & Hart, P. (1967). Nearest neighbor pattern classification. IEEE Transactions on Information Theory, 13(1), 21–27.
11. Elgandelwar, S. M., & Bairagi, V. K. (2021). Power analysis of EEG bands for diagnosis of Alzheimer disease. International Journal of Medical Engineering and Informatics, 13(5), 376–385.
12. Elgendi, M., Vialatte, F., Cichocki, A., Latchoumane, C., Jeong, J., & Dauwels, J. (2011). Optimization of EEG frequency bands for improved diagnosis of Alzheimer disease. 2011 Annual International Conference of the IEEE Engineering in Medicine and Biology Society, 6087–6091.
13. Fiscon, G., Weitschek, E., Cialini, A., Felici, G., Bertolazzi, P., De Salvo, S., Bramanti, A., Bramanti, P., & De Cola, M. C. (2018). Combining EEG signal processing with supervised methods for Alzheimer’s patients classification. BMC Medical Informatics and Decision Making, 18(1), 1–10.
14. Foxe, J. J., & Snyder, A. C. (2011). The role of alpha-band brain oscillations as a sensory suppression mechanism during selective attention. Frontiers in Psychology, 2, 154.
15. Luu, P., Tucker, D. M., Englander, R., Lockfeld, A., Lutsep, H., & Oken, B. (2001). Localizing acute stroke-related eeg changes:: assessing the effects of spatial undersampling. Journal of Clinical Neurophysiology, 18(4), 302–317.
16. Meghdadi, A. H., Stevanović Karić, M., McConnell, M., Rupp, G., Richard, C., Hamilton, J., Salat, D., & Berka, C. (2021). Resting state EEG biomarkers of cognitive decline associated with Alzheimer’s disease and mild cognitive impairment. PloS One, 16(2), e0244180.
17. Michel, C. M., Koenig, T., Brandeis, D., Gianotti, L. R. R., & Wackermann, J. (2009). Electrical neuroimaging. Cambridge University Press.
18. Mitchell, T. M., & Mitchell, T. M. (1997). Machine learning (Vol. 1, Issue 9). McGraw-hill New York.
19. Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., & Dubourg, V. (2011). Scikit-learn: Machine learning in Python. The Journal of Machine Learning Research, 12, 2825–2830.
20. Pineda, A. M., Ramos, F. M., Betting, L. E., & Campanharo, A. S. L. O. (2020). Quantile graphs for EEG-based diagnosis of Alzheimer’s disease. In PLoS ONE (Vol. 15, Issue 6).
21. Poza, J., Hornero, R., Abásolo, D., Fernández, A., & García, M. (2007). Extraction of spectral based measures from MEG background oscillations in Alzheimer’s disease. Medical Engineering & Physics, 29(10), 1073–1083.
22. Riemenschneider, M., Lautenschlager, N., Wagenpfeil, S., Diehl, J., Drzezga, A., & Kurz, A. (2002). Cerebrospinal fluid tau and β-amyloid 42 proteins identify Alzheimer disease in subjects with mild cognitive impairment. Archives of Neurology, 59(11), 1729–1734.
23. Şahin Sadık, E., Saraoğlu, H. M., Canbaz Kabay, S., Tosun, M., Keskinkılıç, C., & Akdağ, G. (2022). Investigation of the effect of rosemary odor on mental workload using EEG: an artificial intelligence approach. Signal, Image and Video Processing, 16(2), 497–504.
24. Şeker, M., Özbek, Y., Yener, G., & Özerdem, M. S. (2021). Complexity of EEG Dynamics for Early Diagnosis of Alzheimer’s Disease Using Permutation Entropy Neuromarker. Computer Methods and Programs in Biomedicine, 206, 106116.