Early Diagnosis of Alzheimer's Disease using Machine Learning Techniques - A Review Paper
Aunsia Khan, Muhammad Usman
2015
Abstract
Alzheimer’s, an irreparable brain disease, impairs thinking and memory while the aggregate mind size shrinks which at last prompts demise. Early diagnosis of AD is essential for the progress of more prevailing treatments. Machine learning (ML), a branch of artificial intelligence, employs a variety of probabilistic and optimization techniques that permits PCs to gain from vast and complex datasets. As a result, researchers focus on using machine learning frequently for diagnosis of early stages of AD. This paper presents a review, analysis and critical evaluation of the recent work done for the early detection of AD using ML techniques. Several methods achieved promising prediction accuracies, however they were evaluated on different pathologically unproven data sets from different imaging modalities making it difficult to make a fair comparison among them. Moreover, many other factors such as pre-processing, the number of important attributes for feature selection, class imbalance distinctively affect the assessment of the prediction accuracy. To overcome these limitations, a model is proposed which comprise of initial pre-processing step followed by imperative attributes selection and classification is achieved using association rule mining. Furthermore, this proposed model based approach gives the right direction for research in early diagnosis of AD and has the potential to distinguish AD from healthy controls.
References
- D, Zhang, 2011. Multimodal classification of Alzheimer's disease and mild cognitive impairment. Neuroimage, 55(3), 856-867.
- Brookmeyer, Ron , Elizabeth Johnson, Kathryn ZieglerGraham, and H. Michael Arrighi, 2007. Forecasting the Global Burden of Alzheimer's Disease. Alzheimer's and Dementia, 3.3, 186-191.
- Brookmeyer, Duda RO, Hart PE, Stork DG. , 2001. Pattern classification. Alzheimer'(2nd edition). New York: Wiley.s and Dementia, 2nd edition.
- T, Mitchell , 1997. Machine Learning. New York: McGraw Hill, 2nd edition, 135.
- SY, Bookheimer, Strojwas MH, Cohen MS, Saunders AM, Pericak-Vance MA, Mazziotta JC, 2000. , et al. Patterns of brain activation in people at risk of Alzheimer's disease. N Engl J Med, 6, 343:450.
- Supekar, K, Menon V, Rubin D, Musen M, Greicius MD, 2008. Network analysis of intrinsic functional brain connectivity in Alzheimer's disease. . PLoS Comput Biol , 4(6), 1-11.
- Cruz, J.A. and D.S. Wishart, 2006. Applications of Machine Learning in Cancer Prediction and Prognosis.Cancer Informatics, 2, 59-77.
- EF, Petricoin, Liotta LA. , 2004. SELDI-TOF-based serum proteomic pattern diagnostics for early detection of cancer. Curr Opin Biotechnol, 15, 24-30.
- Bocchi, L, Coppini G, Nori J, Valli G, 2004. Detection of single and clustered microcalcifications in mammograms using fractals models and neural networks. Med Eng Phys, 26, 303-12.
- Weston, AD, Hood L., 2004. Systems biology, proteomics, and the future of health care: toward predictive, preventative, and personalized medicine. . J Proteome Res, 3, 179-96.
- Bellman, R, 1961. Adaptive Control Processes: A Guided Tour. Princeton University Press, 1, 45.
- Rodvold, DM, McLeod DG, Brandt JM, 2001. Introduction to artificial neural networks for physicians: taking the lid off the black box. Prostate, 46, 39-44.
- Chaves, R., J. Ramírez, et al., 2010. Effective Diagnosis of Alzheimer's Disease by Means of Association Rules. Hybrid Artificial Intelligence Systems, Springer, 1, 452- 459.
- Chaves, R., J. Górriz, et al. (2011). Efficient mining of association rules for the early diagnosis of Alzheimer's disease. Physics in medicine and biology 56(18): 6047.
- Chaves, R., J. Ramírez, et al. (2012). Association rulebased feature selection method for Alzheimer's disease diagnosis. Expert Systems with Applications 39(14): 11766-11774.
- Chaves, R., J. Ramírez, et al. (2012). Functional brain image classification using association rules defined over discriminant regions. Pattern Recognition Letters 33(12): 1666-1672.
- Veeramuthu, A., S. Meenakshi, et al. (2014). A New Approach for Alzheimer's Disease Diagnosis by using Association Rule over PET Images. International Journal of Computer Applications 91(9), 9-14.
- Chaves, R., J. Ramirez, et al. (2012). FDG and PIB biomarker PET analysis for the Alzheimer's disease detection using Association Rules. Nuclear Science Symposium and Medical Imaging Conference (NSS/MIC), IEEE.
- Liu, M., D. Zhang, et al. (2012). Ensemble sparse classification of Alzheimer's disease. Neuroimage 60(2), 1106-1116.
- Hinrichs, C., V. Singh, et al. (2009). Spatially augmented LPboosting for AD classification with evaluations on the ADNI dataset. Neuroimage 48(1), 138-149.
- Cuingnet R1, G. E., Tessieras J, Auzias G, Lehéricy S, Habert MO, Chupin M, Benali H, Colliot O; (2011). Automatic classification of patients with Alzheimer's disease from structural MRI: a comparison of ten methods using the ADNI database. Neuroimage 56, 766-781.
- Chaves, R., J. Ramírez, et al. (2013). Integrating discretization and association rule-based classification for Alzheimer's disease diagnosis. Expert Systems with Applications 40(5), 1571-1578.
- Klöppel, S., C. M. Stonnington, et al. (2008). Automatic classification of MR scans in Alzheimer's disease. Brain 131(3), 681-689.
- Westman, E., J.-S. Muehlboeck, et al. (2012). Combining MRI and CSF measures for classification of Alzheimer's disease and prediction of mild cognitive impairment conversion. Neuroimage 62(1),229-238.
- Westman, Eric, et al. (2011). Multivariate analysis of MRI data for Alzheimer's disease, mild cognitive impairment and healthy controls. Neuroimage 54.2, 1178-1187.
- O. Kohannim, X. Hua, D.P. Hibar, S. Lee, Y.Y. Chou, A.W. Toga, C.R. Jack Jr., M.W. Weiner, P.M. Thompson (2010) Alzheimer's Disease Neuroimaging Initiative Boosting power for clinical trials using classifiers based on multiple biomarkers, Neurobiology of Aging, 31 (8), 1429-1442.
- Polikar, R., C. Tilley, et al. (2010). Multimodal EEG, MRI and PET data fusion for Alzheimer's disease diagnosis. Engineering in Medicine and Biology Society (EMBC), 2010 Annual International Conference of the IEEE, IEEE.
- Parikh, D. and R. Polikar (2007). An ensemble based incremental learning approach to data fusion. Systems, Man, and Cybernetics, Part B: Cybernetics, IEEE Transactions, 37(2), 437-450.
Paper Citation
in Harvard Style
Khan A. and Usman M. (2015). Early Diagnosis of Alzheimer's Disease using Machine Learning Techniques - A Review Paper . In Proceedings of the 7th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management - Volume 1: KDIR, (IC3K 2015) ISBN 978-989-758-158-8, pages 380-387. DOI: 10.5220/0005615203800387
in Bibtex Style
@conference{kdir15,
author={Aunsia Khan and Muhammad Usman},
title={Early Diagnosis of Alzheimer's Disease using Machine Learning Techniques - A Review Paper},
booktitle={Proceedings of the 7th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management - Volume 1: KDIR, (IC3K 2015)},
year={2015},
pages={380-387},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0005615203800387},
isbn={978-989-758-158-8},
}
in EndNote Style
TY - CONF
JO - Proceedings of the 7th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management - Volume 1: KDIR, (IC3K 2015)
TI - Early Diagnosis of Alzheimer's Disease using Machine Learning Techniques - A Review Paper
SN - 978-989-758-158-8
AU - Khan A.
AU - Usman M.
PY - 2015
SP - 380
EP - 387
DO - 10.5220/0005615203800387