Abstract
Early Alzheimer’s Disease (AD) is essential so that may take a preventive step. Current AD detection techniques depend on checks for cognitive impairment that regrettably now no longer offer an accurate prognosis until the affected person has superior beyond a moderate AD. An incorporated and correct gadget for the prognosis and class of mind issues has consequently been suggested. A smartphone is used as a tool to gather sensor datasets. Another hardware tool used is computer and software tools such as python IDE, R studio, and Anaconda. This paper depicts a detailed literature survey on various techniques for identifying AD from the brain image dataset. This survey will help researchers to give direction to their work in this domain. This paper gives a brief idea about various machine learning algorithms and challenges faced during the work. The AD is successfully detected and classified as a result.
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References
Prince M, Albanese E, Guerchet M et al (2014) World Alzheimer Report 2014: dementia and risk reduction an analysis of protective and modifiable factors
Blennow K et al (2006) Alzheimer’s disease. The Lancet 368:387–403
Fargo K, Bleiler L (2014) 2014 Alzheimer’s disease facts and figures. Alzheimers Dement 10:47–92
Yiannopoulou KG, Papageorgiou SG (2013) Current and future treatments for Alzheimer’s disease. Ther Adv Neurol Disord 6(1):19–33
Ullah HMT (2018) Alzheimer’s disease and dementia detection from 3D brain MRI data using deep convolutional. In: 3rd international conference for convergence in technology (I2CT)
He G et al (2019) Alzheimer's disease diagnosis model based on three-dimensional full convolutional DenseNet. In: 10th international conference on information technology in medicine and education (ITME), pp 13–17
Fuse H et al (2018) Detection of Alzheimer's disease with shape analysis of MRI images. In: Joint 10th international conference on soft computing and intelligent systems (SCIS) and 19th international symposium on advanced intelligent systems (ISIS), pp 1031–1034
Shahbaz M, Ali S, Guergachi A, Niazi A, Umer A (2019) Classification of Alzheimer's disease using machine learning techniques . In: International conference on data science, technology and applications (DATA 2019), pp 296–303
Hassan SA, Khan T (2017) A machine learning model to predict the onset of Alzheimer disease using potential cerebrospinal fluid (CSF) biomarkers. Int J Adv Comput Sci Appl (IJACSA) 8(12)
Albright J (2019) Forecasting the progression of Alzheimer’s disease using neural networks and a novel preprocessing algorithm. Alzheimer’s Dement Transl Res Clin Interv 5:483–491
Suresha HS, Parthasarathi S (2019) Relieff feature selection based Alzheimer disease classification using hybrid features and support vector machine in magnetic resonance imaging. Int J Comput Eng Technol 10:124–137
Elshatoury H, Avots E (2019) Volumetric histogram-based alzheimer’s disease using support vector machine. J Alzheimer’s Dis
Zhang Y, Wu L (2012) An MR brain image classifier via principal component analysis and kernel support vector machine. School of Information Science and Engineering, vol 130
Lebedev AV, Westman E, Westen GV, Kramberger MG, Lundervold A, Aarsland D, Soininen H, Kłoszewska I, Mecocci P, Tsolaki M, Vellas B, Lovestone S, Simmons A (2014) Random Forest ensembles for detection and prediction of Alzheimer’s disease with a good between-cohort robustness. NeuroImage: Clin 6:115–125
Fan Z, Xu F, Qi X, Li C, Yao L (2019) Classification of Alzheimer’s disease based on brain MRI and machine learning. Neural Comput Appl 32:1927–1936
Moore PJ, Lyons T, Gallacher J (2019) Random forest prediction of Alzheimer’s disease using pairwise selection from time series data. PLoS ONE, vol 14
Khan A, Zubair S (2019) Usage of random forest ensemble classifier based imputation and its potential in the diagnosis of Alzheimer’s disease. Int J Sci Technol Res 8:271–275
Alzubi OA, Alzubi JA, Tedmori S, Rashaideh H, Almomani O (2018) Consensus-based combining method for classifier ensembles. Int Arab J Inf Technol 15:76–86
Alweshah M, Alzubi OA, Alzubi JA, Alaqeel S (2016) Solving attribute reduction problem using wrapper genetic programming. Int J Comput Sci Netw Secur
Raza M, Awais M, Ellahi W, Aslam N, Nguyen HX, Le HM (2019) Diagnosis and monitoring of Alzheimer’s patients using classical and deep learning techniques. Expert Syst Appl 136:353–364
Padilla P, Lopez M, Ramirez J, Salas-Gonzalez D, Alvarez I (2012) NMF-SVN based CAD tool applied to functional brain images for the diagnosis of Alzheimer’s disease. IEEE Trans Med Imaging 31
Xu L, Liang G, Liao C, Chen G, Chang C (2019) K-Skip-n-gram-RF: a random forest based method for Alzheimer's disease protein identification. Front Genet 10
Tait L, Tamagnini F, Stothart G, Barvas E, Monaldini C, Frusciante R, Volpini M, Guttmann S, Coulthard E, Brown JT, Kazanina N, Goodfellow M (2020) EEG microstate complexity for aiding early diagnosis of Alzheimer’s disease. Sci Rep 10
Kulkarni N, Bairagi V (2017) Extracting salient features for EEG-based diagnosis of Alzheimer’s disease using support vector machine classifier. IETE J Res 63:11–22
Zhang Y, Wang S, Dong Z (2014) Classification of Alzheimer disease based on structural magnetic resonance imaging by Kernel support vector machine decision tree. Prog Electromagn Res 144:171–184
Zhang Y, Wu L (2012) An MR brain images classifier via principal component analysis and kernel support vector machine. Prog Electromagn Res 130:369–388
Vidushi AR, Shrivastava AK (2020) Diagnosis of Alzheimer disease using machine learning approaches. Int J Adv Sci Technol 2(4)
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Mane, D.T., Patel, M., Sawant, M., Maiyani, K., Patil, D. (2022). A Survey on Alzheimer’s Disease Detection and Classification. In: Nanda, P., Verma, V.K., Srivastava, S., Gupta, R.K., Mazumdar, A.P. (eds) Data Engineering for Smart Systems. Lecture Notes in Networks and Systems, vol 238. Springer, Singapore. https://doi.org/10.1007/978-981-16-2641-8_60
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