The significance of data analytics and machine learning has increased in managing vast quantities of healthcare data effectively. According to recent statistics, Alzheimer’s disease is the most prevalent cause of dementia worldwide. Moreover, Alzheimer’s arises in some people with increasing mild cognitive impairment (MCI). Early detection and treatment of Alzheimer’s disease and its prodromal stage, also known as MCI, is vital to prevent its progression. For selecting the most relevant features, the four feature selection methods, including Mutual Information (MI), Recursive Feature Elimination (RFE), -test, and Genetic algorithm (GA), are used. Also, three classification methods include Adaboost, random forest, and support vector machine (SVM) with the linear kernel; Moreover, a resnet network is applied to categorize the patients as cognitive normal, MCI, which includes both progressive (pMCI) and stable (sMCI) cases, and Alzheimer’s disease using positron emission tomography (PET) scan images. Among these machine learning methods, combining a -test and a genetic algorithm for selecting the most relevant features and applying a support vector machine with 8-fold cross-validation have produced the best results on high-dimensional images of Alzheimer’s disease neuroimaging initiative (ADNI). The proposed method differentiates between sMCI and pMCI patients with a 95.45% accuracy rate and 95.23% F1-score, outperforms the performance of recent studies, as well as AD and CN with 97.36% accuracy rate and 100% recall, making it acceptable for supporting clinical applications.
RavisankarP, et al. Alzheimer’s – a detailed study on causes, symptoms, remedies and current research studies. IOSR J. od Dent. Med. Sci.2016; 15(2): 108–121. doi: 10.9790/0853-1525108121.
2.
DavatzikosCResnickSCWuXParmpiPClarkCM. Individual Patient Diagnosis of AD and FTD via High-Dimensional Pattern Classification of MRI. 2008; 41(4): 1220–1227. doi: 10.1016/j.neuroimage.2008.03.050.
3.
ToddSBarrSRobertsMPassmoreAP. Survival in dementia and predictors of mortality: A review. International Journal of Geriatric Psychiatry. 2013; 28(11): 1109–1124. doi: 10.1002/gps.3946.
4.
TanveerM, et al. Machine learning techniques for the diagnosis of Alzheimer’s disease: A review. ACM Trans. Multimed. Comput. Commun. Appl.2020; 16(1s): 1–22. doi: 10.1145/3344998.
5.
MwangiBTianTSSoaresJC. A review of feature reduction techniques in Neuroimaging. Neuroinformatics. 2014; 12(2): 229–244. doi: 10.1007/s12021-013-9204-3.
6.
ZengNQiuHWangZLiuWZhangHLiY. A new switching-delayed-PSO-based optimized SVM algorithm for diagnosis of Alzheimer’s disease. Neurocomputing. 2018; 320: 195–202. doi: 10.1016/j.neucom.2018.09.001.
BrownRKJBohnenNIWongKKMinoshimaSFreyKA. Brain PET in suspected dementia: Patterns of altered FDG metabolism. Radiographics. 2014; 34(3): 684–701. doi: 10.1148/rg.343135065.
9.
MinhasSKhanumARiazFAlviAKhanSA. A nonparametric approach for mild cognitive impairment to AD conversion prediction: Results on longitudinal data. IEEE J Biomed Health Inform. 2017; 21(5): 1403–1410. doi: 10.1109/JBHI.2016.2608998.
10.
ChaplotSPatnaikLMJagannathanNR. Classification of magnetic resonance brain images using wavelets as input to support vector machine and neural network. Biomed Signal Process Control. 2006; 1(1): 86–92. doi: 10.1016/j.bspc.2006.05.002.
11.
BeheshtiIDemirelHMatsudaH. Classification of Alzheimer’s disease and prediction of mild cognitive impairment-to-Alzheimer’s conversion from structural magnetic resource imaging using feature ranking and a genetic algorithm. Comput Biol Med. 2017; 83(November 2016): 109–119. doi: 10.1016/j.compbiomed.2017.02.011.
12.
SukHIShenD. Subclass-based multi-task learning for Alzheimer’s disease diagnosis. Front Aging Neurosci. 2014; 6(JUN): 1–20. doi: 10.3389/fnagi.2014.00168.
ErshadiMMSeifiA. An efficient multi-classifier method for differential diagnosis. Intelligent Decision Technologies. 2020; 14(3): 337–347.
15.
BasaiaS, et al. Automated classification of Alzheimer’s disease and mild cognitive impairment using a single MRI and deep neural networks. NeuroImage Clin. 2019; 21(November 2018): 101645. doi: 10.1016/j.nicl.2018.101645.
16.
WangH, et al. Ensemble of 3D densely connected convolutional network for diagnosis of mild cognitive impairment and Alzheimer’s disease. Neurocomputing. 2019; 333: 145–156. doi: 10.1016/j.neucom.2018.12.018.
LiuS, et al. Multimodal neuroimaging feature learning for multiclass diagnosis of Alzheimer’s disease. IEEE Trans Biomed Eng. 2015; 62(4): 1132–1140. doi: 10.1109/TBME.2014.2372011.
19.
LuDPopuriKDingGWBalachandarRBegMF. Multiscale deep neural network based analysis of FDG-PET images for the early diagnosis of Alzheimer’s disease. Med Image Anal. 2018; 46: 26–34. doi: 10.1016/j.media.2018.02.002.
20.
SpasovSPassamontiLDuggentoALiòPToschiN. A parameter-efficient deep learning approach to predict conversion from mild cognitive impairment to Alzheimer’s disease. Neuroimage. 2019; 189: 276–287. doi: 10.1016/j.neuroimage.2019.01.031.
21.
Ashtari-MajlanMSeifiADehshibiMM. A multi-stream convolutional neural network for classification of progressive MCI in Alzheimer’s disease using structural MRI images. IEEE J Biomed Health Inform. 2022; 2194(c): 1–10. doi: 10.1109/JBHI.2022.3155705.
22.
Van VeenR, et al. Machine learning based analysis of FDG-PET image data for the diagnosis of neurodegenerative diseases. Front Artif Intell Appl. 2018; 310(January): 280–289. doi: 10.3233/978-1-61499-929-4-280.
23.
ChengBLiuMSukHIShenDZhangD. Multimodal manifold-regularized transfer learning for MCI conversion prediction. Brain Imaging Behav. 2015; 9(4): 913–926. doi: 10.1007/s11682-015-9356-x.
24.
DukartJMuellerKBarthelHVillringerASabriOSchroeterML. Alzheimer’s Disease Neuroimaging Initiative. Meta-analysis based SVM classification enables accurate detection of Alzheimer’s disease across different clinical centers using FDG-PET and MRI. Psychiatry Res Neuroimaging. 2013; 212(3): 230–236.
25.
HinrichsCSinghVMukherjeeLXuGChungMKJohnsonSC. Spatially augmented LPboosting for AD classification with evaluations on the ADNI dataset. Neuroimage. 2009; 48(1): 138–149. doi: 10.1016/j.neuroimage.2009.05.056.
26.
SegoviaF, et al. Classification of functional brain images using a GMM-based multi-variate approach. Neurosci Lett. 2010; 474(1): 58–62. doi: 10.1016/j.neulet.2010.03.010.
27.
SukHILeeSWShenD. Latent feature representation with stacked auto-encoder for AD/MCI diagnosis. Brain Struct Funct. 2015; 220(2): 841–859. doi: 10.1007/s00429-013-0687-3.
28.
WestmanE, et al. AddNeuroMed and ADNI: Similar patterns of Alzheimer’s atrophy and automated MRI classification accuracy in Europe and North America. Neuroimage. 2011; 58(3): 818–828. doi: 10.1016/j.neuroimage.2011.06.065.
29.
PanX, et al. Multi-View Separable Pyramid Network for AD Prediction at MCI Stage by 18F-FDG Brain PET Imaging. IEEE Trans Med Imaging. 2021; 40(1): 81–92. doi: 10.1109/TMI.2020.3022591.
30.
SinghN, et al. Quantifying anatomical shape variations in neurological disorders. Med Image Anal. 2014; 18(3): 616–633. doi: 10.1016/j.media.2014.01.001.
31.
GaraliIAdelMBourennaneSGuedjE. Brain region ranking for 18FDG-PET computer-aided diagnosis of Alzheimer’s disease. Biomed Signal Process Control. 2016; 27: 15–23. doi: 10.1016/j.bspc.2016.01.009.
32.
ZitováBFlusserJ. Image registration methods: A survey. Image Vis Comput. 2003; 21(11): 977–1000. doi: 10.1016/S0262-8856(03)00137-9.
33.
SunX, et al. A human brain tau PET template in MNI space for the voxel-wise analysis of Alzheimer’s disease. J Neurosci Methods. 2019; 328: 108438. doi: 10.1016/j.jneumeth.2019.108438.
34.
ZhouNWangL. A modified T-test feature selection method and its application on the HapMap genotype data. Genomics Proteomics Bioinforma. 2007; 5(3–4): 242–249. doi: 10.1016/S1672-0229(08)60011-X.
35.
PohjalainenJRäsänenOKadiogluS. Feature selection methods and their combinations in high-dimensional classification of speaker likability, intelligibility and personality traits. Comput. Speech Lang. 2015; 29(1): 145–171. doi: 10.1016/j.csl.2013.11.004.
SlavkovIZenkoBDzeroskiS. Evaluation method for feature rankings and their aggregations for biomarker discovery. J. Mach. Learn. Res. – Proc. Track.2010; 8: 122–135.
38.
JinZShangJZhuQLingCXieWQiangB. RFRSF: Employee turnover prediction based on random forests and survival analysis. Lect. Notes Comput. Sci. (including Subser. Lect. Notes Artif. Intell. Lect. Notes Bioinformatics).2020; 12343 LNCS: 503–515. doi: 10.1007/978-3-030-62008-0_35.
39.
DezhenZKaiY. Genetic algorithm based optimization for AdaBoost. Proc. – Int. Conf. Comput. Sci. Softw. Eng. CSSE 2008.2008; 1: 1044–1047. doi: 10.1109/CSSE.2008.1040.
40.
ShirdelGGhanbariA. A survey on self-supervised learning methods for domain adaptation in deep neural networks focusing on the optimization problems. 2022; 3(2): 217–235. doi: 10.22060/AJMC.2022.21221.1079.
41.
HeKZhangXRenSSunJ. Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 2016. pp. 770–778.
42.
Al-NaamiNGharaibehAKheshmanA. Automated detection of Alzheimer disease using region growing technique and artificial neural network. Int J Biomed Biol Eng. 2013; 7(5): 204–208.
43.
MahmoodRGhimireB. Automatic detection and classification of Alzheimer’s disease from MRI scans using principal component analysis and artificial neural networks. In: International Conference on Systems, Signals, and Image Processing. IEEE; 2013. pp. 133–137. doi: 10.1109/IWSSIP.2013.6623471.
44.
KarSMajumderDD. A novel approach of diffusion tensor visualization based neuro fuzzy classification system for early detection of Alzheimer’s disease. J. Alzheimer’s Dis. Reports.2019; 3(1): 1–18. doi: 10.3233/adr-180082.
45.
Van VeenRVan Der KleinPMeeuwissenE, et al. Machine learning based analysis of FDG-PET image data for the diagnosis of neurodegenerative diseases. Front. Artif. Intell. Appl.2018; 310(1): 280–289. doi: 10.3233/978-1-61499-929-4-280.
46.
OrtizAGórrizJMRamírezJMartinez-MurciaFJ. Automatic ROI selection in structural brain MRI using SOM 3D projection. PLoS One. 2014; 9(4): e93851. doi: 10.1371/journal.pone.0093851.
47.
GorjiHTHaddadniaJ. A novel method for early diagnosis of Alzheimer’s disease based on pseudo Zernike moment from structural MRI. Neuroscience. 2015; 305: 361–371. doi: 10.1016/j.neuroscience.2015.08.013.
48.
JhaDKimJIKwonGR. Diagnosis of Alzheimer’s disease using dual-tree complex wavelet transform, PCA, and feed-forward neural network. J. Healthc. Eng. 2017; 2017: 9060124. doi: 10.1155/2017/9060124.
49.
GhoshSDasNDasIMaulikU. Understanding deep learning techniques for image segmentation. ACM Comput. Surv. 2019; 52(4). doi: 10.1145/3329784.
50.
HornJF, et al. Differential automatic diagnosis between Alzheimer’s disease and frontotemporal dementia based on perfusion SPECT images. Artif. Intell. Med.2009; 47(2): 147–158. doi: 10.1016/j.artmed.2009.05.001.
51.
LegerS, et al. A comparative study of machine learning methods for time-to-event survival data for radiomics risk modeling. Sci. Rep.2017; 7(1): 1–11. doi: 10.1038/s41598-017-13448-3.
52.
BhatkotiPPaulM. Early diagnosis of Alzheimer’s disease: A multi-class deep learning framework with modified k-sparse autoencoder classification. In: 2016 International Conference on Image and Vision Computing New Zealand (IVCNZ). 2016. pp. 1–5. doi: 10.1109/IVCNZ.2016.7804459.
53.
HuCJuRShenYZhouPLiQ. Clinical decision support for Alzheimer’s disease based on deep learning and brain network. In: 2016 IEEE International Conference on Communications (ICC). doi: 10.1109/ICC.2016.7510831.
54.
ShiJZhengXLiYZhangQYingS. Multimodal neuroimaging feature learning with multimodal stacked deep polynomial networks for diagnosis of Alzheimer’s disease. IEEE Journal of Biomedical and Health Informatics. 2018; 22(1): 173–183. doi: 10.1109/JBHI.2017.2655720.
55.
SukHILeeSWShenD. Deep sparse multi-task learning for feature selection in Alzheimer’s disease diagnosis. Brain Structure and Function. 2016; 221(5): 2569–2587. doi: 10.1007/s00429-015-1059-y.
56.
SukHILeeSWShenD. Deep ensemble learning of sparse regression models for brain disease diagnosis. Medical Image Analysis. 2017; 37: 101–113. doi: 10.1016/j.media.2017.01.008.
57.
AlamSKwonGR. Alzheimer disease classification using KPCA, LDA, and multi-kernel learning SVM. International Journal of Imaging Systems and Technology. 2017; 27(2): 133–143. doi: 10.1002/ima.22217.
58.
SukHIWeeCYLeeSWShenD. State-space model with deep learning for functional dynamics estimation in resting-state fMRI. NeuroImage. 2016; 129: 292–307. doi: 10.1016/j.neuroimage.2016.01.005.
59.
LiuMZhangJAdeliEShenD. Joint classification and regression via deep multi-task multi-channel learning for Alzheimer’s disease diagnosis. IEEE Trans Biomed Eng. 2019; 66(5): 1195–1206. doi: 10.1109/TBME.2018.2869989.
60.
CabralCMorgadoPMCampos CostaDSilveiraM. Predicting conversion from MCI to AD with FDG-PET brain images at different prodromal stages. Comput Biol Med. 2015; 58: 101–109. doi: 10.1016/j.compbiomed.2015.01.003.
61.
ZhaoYXZhangYMSongMLiuCL. Region ensemble network for MCI conversion prediction with a relation regularized loss. Springer International Publishing. 2021; 24: 185–194. doi: 10.1007/978-3-030-87240-3_18.
