Prediction Modeling of Alzheimer’s Disease and Its Prodromal Stages from Multimodal Data with Missing Values
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Prediction Modeling of Alzheimer’s Disease and Its Prodromal Stages from Multimodal Data with Missing Values

Authors: M. Aghili, S. Tabarestani, C. Freytes, M. Shojaie, M. Cabrerizo, A. Barreto, N. Rishe, R. E. Curiel, D. Loewenstein, R. Duara, M. Adjouadi


A major challenge in medical studies, especially those that are longitudinal, is the problem of missing measurements which hinders the effective application of many machine learning algorithms. Furthermore, recent Alzheimer's Disease studies have focused on the delineation of Early Mild Cognitive Impairment (EMCI) and Late Mild Cognitive Impairment (LMCI) from cognitively normal controls (CN) which is essential for developing effective and early treatment methods. To address the aforementioned challenges, this paper explores the potential of using the eXtreme Gradient Boosting (XGBoost) algorithm in handling missing values in multiclass classification. We seek a generalized classification scheme where all prodromal stages of the disease are considered simultaneously in the classification and decision-making processes. Given the large number of subjects (1631) included in this study and in the presence of almost 28% missing values, we investigated the performance of XGBoost on the classification of the four classes of AD, NC, EMCI, and LMCI. Using 10-fold cross validation technique, XGBoost is shown to outperform other state-of-the-art classification algorithms by 3% in terms of accuracy and F-score. Our model achieved an accuracy of 80.52%, a precision of 80.62% and recall of 80.51%, supporting the more natural and promising multiclass classification.

Keywords: eXtreme Gradient Boosting, missing data, Alzheimer disease, early mild cognitive impairment, late mild cognitive impairment, multiclass classification, ADNI, support vector machine, random forest.

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[1] J. Karlawish, C. R. Jack, W. A. Rocca, H. M. Snyder, and M. C. Carrillo, “2017 Alzheimer’s disease facts and figures,” Alzheimer’s Dement., 2017.
[2] Cuingnet R, Gerardin E, Tessieras J, Auzias G, Lehéricy S, Habert M-O, Chupin M, Benali H, Colliot O, “Automatic classification of patients with Alzheimer’s disease from structural MRI: A comparison of ten methods using the ADNI database,” Neuroimage, vol. 56, no. 2, pp. 766–781, 2011.
[3] K. Ritter, J. Schumacher, M. Weygandt, R. Buchert, C. Allefeld, and J. D. Haynes, “Multimodal prediction of conversion to Alzheimer’s disease based onincomplete biomarkers,” Alzheimer’s Dement. Diagnosis, Assess. Dis. Monit., vol. 1, no. 2, pp. 206–215, 2015.
[4] H. Zhang, F. Zhu, H. H. Dodge, G. A. Higgins, G. S. Omenn, and Y. Guan, “A similarity-based approach to leverage multi-cohort medical data on the diagnosis and prognosis of Alzheimer’s disease,” Gigascience, vol. 7, no. 7, pp. 1–10, 2018.
[5] A. Farzan, S. Mashohor, A. R. Ramli, and R. Mahmud, “Boosting diagnosis accuracy of Alzheimer’s disease using high dimensional recognition of longitudinal brain atrophy patterns,” Behav. Brain Res., vol. 290, pp. 124–130, 2015.
[6] S. Tabarestani, M. Eslami, and F. Torkamni-Azar, “Painting style classification in Persian Miniatures,” in Iranian Conference on Machine Vision and Image Processing, MVIP, 2016.
[7] L. Ghorbanzadeh and A. E. Torshabi, "An Investigation into the Performance of Adaptive Neuro-Fuzzy Inference System for Brain Tumor Delineation Using ExpectationMaximization Cluster Method; a Feasibility Study," Frontiers in Biomedical Technologies, vol. 3, pp. 8-19, 2017.
[8] S. Tabarestani, M. Aghili, M. Shojaie, C. Freytes, “Profile-Specific Regression Model for Progression Prediction of Alzheimer’s Disease Using Longitudinal Data,” 17th IEEE Int. Conf. Mach. Learn. Appl., 2018.
[9] R. C. Petersen and J. C. Morris, “Mild cognitive impairment as a clinical entity and treatment target,” Arch. Neurol., vol. 62, no. 7, pp. 1160–1163, 2005.
[10] E. Moradi, A. Pepe, C. Gaser, H. Huttunen, and J. Tohka, “Machine learning framework for early MRI-based Alzheimer’s conversion prediction in MCI subjects,” Neuroimage, vol. 104, pp. 398–412, 2015.
[11] W. Izquierdo et al., "Robust prediction of cognitive test scores in Alzheimer's patients," 2017 IEEE Signal Processing in Medicine and Biology Symposium (SPMB), Philadelphia, PA, 2017, pp. 1-7.
[12] H. Il Suk, S. W. Lee, and D. Shen, “Hierarchical feature representation and multimodal fusion with deep learning for AD/MCI diagnosis,” Neuroimage, vol. 101, pp. 569–582, 2014.
[13] M. Aghili, S. Tabarestani, M. Adjouadi, and E. Adeli, “Predictive Modeling of Longitudinal Data for Alzheimer’s Disease Diagnosis Using RNNs,” in PRedictive Intelligence in MEdicine, 2018, pp. 112–119.
[14] M. Mafi, S. Tabarestani, M. Cabrerizo, A. Barreto and M. Adjouadi, "Denoising of ultrasound images affected by combined speckle and Gaussian noise," in IET Image Processing, vol. 12, no. 12, pp. 2346-2351, 12 2018.
[15] W. Izquierdo, H. Martin, M. Cabrerizo, A. Barreto, J. Andrian, N. Rishe, S. Gonzalez-Arias, D. Loewenstein, R. Duara, M. Adjouadi, "Robust prediction of cognitive test scores in Alzheimer's patients," 2017 IEEE Signal Processing in Medicine and Biology Symposium (SPMB), Philadelphia, PA, 2017, pp. 1-7.
[16] S. Natarajan, B. Saha, S. Joshi, A. Edwards, T. Khot, E. M. Davenport, K. Kersting, C. T. Whitlow, and J. A. Maldjian. "Relational learning helps in three-way classification of Alzheimer patients from structural magnetic resonance images of the brain," Intl. Journal of Machine Learning and Cybernetics, pages 1–11, 2013.
[17] J. H. Friedman. "Greedy function approximation:a gradient boosting machine," The Annals of Statistics, 29(5):1189–1232, 2001.
[18] M. W. Weiner, D. P. Veitch, P. S. Aisen et al., "The Alzheimer's disease neuroimaging initiative: a review of papers published since its inception", Alzheimer's Dement., vol. 9, no. 5, pp. e111-e194, 2013.
[19] M. Eslami, F. Torkamani-Azar, and E. Mehrshahi, “A Centralized PSD Map Construction by Distributed Compressive Sensing,” IEEE Commun. Lett., 2015.
[20] M. Eslami, A. H. Gazestani, and S. A. Ghorashi, “Introduction and Patent Analysis of Signal Processing for Big Data,” Adv. Parallel Comput., vol. 33, no. Big Data and HPC: Ecosystem and Convergence, pp. 101-–119, 2018.
[21] T. Chen and C. Guestrin, “Xgboost: A scalable tree boosting system,” arXiv preprint arXiv:1603.02754, 2016.
[22] T. Klikauer, “Scikit-learn: Machine Learning in Python,” TripleC, 2016.