Examining stability of machine learning methods for predicting dementia at early phases of the disease

Abstract: Dementia is a neuropsychiatric brain disorder that usually occurs when one or more brain cells stop working partially or at all. Diagnosis of this disorder in the early phases of the disease is a vital task to rescue patients’ lives from bad consequences and provide them with better healthcare. Machine learning methods have been proven to be accurate in predicting dementia in the early phases of the disease. The prediction of dementia depends heavily on the type of collected data which usually are gathered from Normalized Whole Brain Volume (nWBV) and Atlas Scaling Factor (ASF) which are normally measured and corrected from Magnetic Resonance Imaging (MRIs). Other biological features such as age and gender can also help in the diagnosis of dementia. Although many studies use machine learning for predicting dementia, we could not reach a conclusion on the stability of these methods for which one is more accurate under different experimental conditions. Therefore, this paper investigates the conclusion stability regarding the performance of machine learning algorithms for dementia prediction. To accomplish this, a large number of experiments were run using 7 machine learning algorithms and two feature reduction algorithms namely, Information Gain (IG) and Principal Component Analysis (PCA). To examine the stability of these algorithms, thresholds of feature selection were changed for the IG from 20% to 100% and the PCA dimension from 2 to 8. This has resulted in 7×9 + 7×7= 112 experiments. In each experiment, various classification evaluation data were recorded. The obtained results show that among seven algorithms the support vector machine and Naïve Bayes are the most stable algorithms while changing the selection threshold. Also, it was found that using IG would seem more efficient than using PCA for predicting Dementia. These promising results open the door to a new era of early prognosis of Alzheimer’s Disease and Related Dementias (ADRD).

How to cite this paper

 Faouri, S., AlBashayreh, M & Azzeh, M. (2022). Examining stability of machine learning methods for predicting dementia at early phases of the disease.Decision Science Letters , 11(3), 333-346.

Refrences

Abdi, H., & Williams, L. J. (2010). Principal component analysis. WIREs Computational Statistics, 2(4), 433–459. https://doi.org/https://doi.org/10.1002/wics.101
Alam, M. A. U., Roy, N., Holmes, S., Gangopadhyay, A., & Galik, E. (2016). Automated Functional and Behavioral Health Assessment of Older Adults with Dementia. 2016 IEEE First International Conference on Connected Health: Applications, Systems and Engineering Technologies (CHASE), 140–149. https://doi.org/10.1109/CHASE.2016.16
Babiloni, C., Del Percio, C., Lizio, R., Noce, G., Cordone, S., Lopez, S., Soricelli, A., Ferri, R., Pascarelli, M. T., Nobili, F., Arnaldi, D., Aarsland, D., Orzi, F., Buttinelli, C., Giubilei, F., Onofrj, M., Stocchi, F., Stirpe, P., Fuhr, P., … Bonanni, L. (2017). Abnormalities of cortical neural synchronization mechanisms in patients with dementia due to Alzheimer’s and Lewy body diseases: an EEG study. Neurobiology of Aging, 55, 143–158. https://doi.org/10.1016/j.neurobiolaging.2017.03.030
Bansal, D., Chhikara, R., Khanna, K., & Gupta, P. (2018). Comparative Analysis of Various Machine Learning Algorithms for Detecting Dementia. Procedia Computer Science, 132, 1497–1502. https://doi.org/10.1016/j.procs.2018.05.102
Bansal, D., Khanna, K., Chhikara, R., Dua, R. K., & Malhotra, R. (2020). Classification of Magnetic Resonance Images using Bag of Features for Detecting Dementia. Procedia Computer Science, 167(2019), 131–137. https://doi.org/10.1016/j.procs.2020.03.190
Barnes, J., Dickerson, B. C., Frost, C., Jiskoot, L. C., Wolk, D., & van der Flier, W. M. (2015). Alzheimer’s disease first symptoms are age-dependent: Evidence from the NACC dataset. Alzheimer’s & Dementia : The Journal of the Alzheimer’s Association, 11(11), 1349–1357. https://doi.org/10.1016/j.jalz.2014.12.007
Battineni, G., Chintalapudi, N., & Amenta, F. (2019). Machine learning in medicine: Performance calculation of dementia prediction by support vector machines (SVM). Informatics in Medicine Unlocked, 16, 100200. https://doi.org/10.1016/j.imu.2019.100200
Battineni, G., Chintalapudi, N., Amenta, F., & Traini, E. (2020). A Comprehensive Machine-Learning Model Applied to Magnetic Resonance Imaging (MRI) to Predict Alzheimer’s Disease (AD) in Older Subjects. Journal of Clinical Medicine, 9(7), 2146. https://doi.org/10.3390/jcm9072146
Bharanidharan, N., & Rajaguru, H. (2020). Performance enhancement of swarm intelligence techniques in dementia classification using dragonfly-based hybrid algorithms. In International Journal of Imaging Systems and Technology (Vol. 30, Issue 1, pp. 57–74). https://doi.org/10.1002/ima.22365
Boysen, J. (2017). Magnetic Resonance Imaging Comparisons of Demented and Nondemented Adults. Kaggle. https://www.kaggle.com/jboysen/mri-and-alzheimers
Chen, R., & Herskovits, E. H. (2010). Machine-learning techniques for building a diagnostic model for very mild dementia. NeuroImage, 52(1), 234–244. https://doi.org/10.1016/j.neuroimage.2010.03.084
Dai, D. L., Tropea, T. F., Robinson, J. L., Suh, E., Hurtig, H., Weintraub, D., Van Deerlin, V., Lee, E. B., Trojanowski, J. Q., & Chen-Plotkin, A. S. (2020). ADNC-RS, a clinical-genetic risk score, predicts Alzheimer’s pathology in autopsy-confirmed Parkinson’s disease and Dementia with Lewy bodies. Acta Neuropathologica, 140(4), 449–461. https://doi.org/10.1007/s00401-020-02199-7
Dallora, A. L., Minku, L., Mendes, E., Rennemark, M., Anderberg, P., & Berglund, J. S. (2020). Multifactorial 10-year prior diagnosis prediction model of dementia. International Journal of Environmental Research and Public Health, 17(18), 1–18. https://doi.org/10.3390/ijerph17186674
Garrard, P., Rentoumi, V., Gesierich, B., Miller, B., & Gorno-Tempini, M. L. (2014). Machine learning approaches to diagnosis and laterality effects in semantic dementia discourse. Cortex, 55, 122–129. https://doi.org/10.1016/j.cortex.2013.05.008
Green, C., & Zhang, S. (2016). Predicting the progression of Alzheimer’s disease dementia: A multidomain health policy model. Alzheimer’s & Dementia : The Journal of the Alzheimer’s Association, 12(7), 776–785. https://doi.org/10.1016/j.jalz.2016.01.011
Harvey, R. J., Skelton-Robinson, M., & Rossor, M. N. (2003). The prevalence and causes of dementia in people under the age of 65 years. Journal of Neurology, Neurosurgery, and Psychiatry, 74(9), 1206–1209. https://doi.org/10.1136/jnnp.74.9.1206
KENT, J. T. (1983). Information gain and a general measure of correlation. Biometrika, 70(1), 163–173. https://doi.org/10.1093/biomet/70.1.163
Kleinbaum, D. G., & Klein, M. (2010). Logistic Regression. Springer New York. https://doi.org/10.1007/978-1-4419-1742-3
Lakshmi, P. V. (2020). An application of machine learning strategies to predict Alzheimer’s illness progression in patients. August. https://doi.org/10.34218/IJARET.11.6.2020.95
Malone, I., Leung, K., Clegg, S., Barnes, J., Whitwell, J., Ashburner, J., Fox, N., & Ridgway, G. (2014). Accurate automatic estimation of total intracranial volume: A nuisance variable with less nuisance. NeuroImage, 104. https://doi.org/10.1016/j.neuroimage.2014.09.034
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. https://doi.org/10.1162/jocn.2009.21407
Minku, L., Technology, X. Y.-I. and S., & 2013, U. (2013). Ensembles and locality: Insight on improving software effort estimation. Information and Software Technology, 55(8), 1512–1528.
Myles, A. J., Feudale, R. N., Liu, Y., Woody, N. A., & Brown, S. D. (2004). An introduction to decision tree modeling. In Journal of Chemometrics (Vol. 18, Issue 6, pp. 275–285). John Wiley & Sons, Ltd. https://doi.org/10.1002/cem.873
Pearson, K. (1901). LIII. On lines and planes of closest fit to systems of points in space. The London, Edinburgh, and Dublin Philosophical Magazine and Journal of Science, 2(11), 559–572. https://doi.org/10.1080/14786440109462720
Pellegrini, E., Ballerini, L., Hernandez, M. del C. V., Chappell, F. M., González-Castro, V., Anblagan, D., Danso, S., Muñoz-Maniega, S., Job, D., Pernet, C., Mair, G., MacGillivray, T. J., Trucco, E., & Wardlaw, J. M. (2018). Machine learning of neuroimaging for assisted diagnosis of cognitive impairment and dementia: A systematic review. Alzheimer’s & Dementia: Diagnosis, Assessment & Disease Monitoring, 10, 519–535. https://doi.org/10.1016/j.dadm.2018.07.004
Popuri, K., Ma, D., Wang, L., & Beg, M. F. (2020). Using machine learning to quantify structural MRI neurodegeneration patterns of Alzheimer’s disease into dementia score: Independent validation on 8,834 images from ADNI, AIBL, OASIS, and MIRIAD databases. Human Brain Mapping, 41(14), 4127–4147. https://doi.org/10.1002/hbm.25115
Qi, Y. (2012). Random Forest for Bioinformatics. In Ensemble Machine Learning (pp. 307–323). Springer US. https://doi.org/10.1007/978-1-4419-9326-7_11
Raileanu, L. E., & Stoffel, K. (2004). Theoretical Comparison between the Gini Index and Information Gain Criteria. Annals of Mathematics and Artificial Intelligence, 41(1), 77–93. https://doi.org/10.1023/B:AMAI.0000018580.96245.c6
Rätsch, G., Onoda, T., & Müller, K. R. (2001). Soft margins for AdaBoost. Machine Learning, 42(3), 287–320. https://doi.org/10.1023/A:1007618119488
Ringnér, M. (2008). What is principal component analysis? Nature Biotechnology, 26(3), 303–304. https://doi.org/10.1038/nbt0308-303
Sharma, N., Kolekar, M., & Jha, K. (2020). Iterative Filtering Decomposition based Early Dementia Diagnosis using EEG with Cognitive Tests. IEEE Transactions on Neural Systems and Rehabilitation Engineering, 4320(c), 1–1. https://doi.org/10.1109/TNSRE.7333
Soria, D., Garibaldi, J. M., Ambrogi, F., Biganzoli, E. M., & Ellis, I. O. (2011). A “non-parametric” version of the naive Bayes classifier. Knowledge-Based Systems, 24(6), 775–784. https://doi.org/10.1016/j.knosys.2011.02.014
van de Vorst, I. E., Golüke, N. M. S., Vaartjes, I., Bots, M. L., & Koek, H. L. (2020). A prediction model for one- and three-year mortality in dementia: results from a nationwide hospital-based cohort of 50,993 patients in the Netherlands. Age and Ageing, 49(3), 361–367. https://doi.org/10.1093/ageing/afaa007
WHO. (2020). World Health Organization.
Widodo, A., & Yang, B. S. (2007). Support vector machine in machine condition monitoring and fault diagnosis. In Mechanical Systems and Signal Processing (Vol. 21, Issue 6, pp. 2560–2574). Academic Press. https://doi.org/10.1016/j.ymssp.2006.12.007
Wu, Y., Ianakiev, K., & Govindaraju, V. (2002). Improved k-nearest neighbor classification. Pattern Recognition, 35(10), 2311–2318. https://doi.org/10.1016/S0031-3203(01)00132-7
You, Y., Ahmed, B., Barr, P., Ballard, K., & Valenzuela, M. (2019). Predicting Dementia Risk Using Paralinguistic and Memory Test Features with Machine Learning Models. 2019 IEEE Healthcare Innovations and Point of Care Technologies, HI-POCT 2019, 56–59. https://doi.org/10.1109/HI-POCT45284.2019.8962887
Zriqat, I. A., Altamimi, A. M., & Azzeh, M. (2017). A Comparative Study for Predicting Heart Diseases Using Data Mining Classification Methods.