IJBTT

Alzheimer’s Disease Diagnosis using Machine Learning: A Review

Alzheimer’s Disease Diagnosis using Machine Learning: A Review
	© 2023 by IJETT Journal
	Volume-71 Issue-3
	Year of Publication : 2023
	Author : Nair Bini Balakrishnan, P.S. Sreeja, Jisha Jose Panackal
	DOI : 10.14445/22315381/IJETT-V71I3P213

How to Cite?

Nair Bini Balakrishnan, P.S. Sreeja, Jisha Jose Panackal, "Alzheimer’s Disease Diagnosis using Machine Learning: A Review," International Journal of Engineering Trends and Technology, vol. 71, no. 3, pp. 120-129, 2023. Crossref, https://doi.org/10.14445/22315381/IJETT-V71I3P213

Abstract
Alzheimer’s Disease (AD) is an acute neuro disease that degenerates the brain cells and thus leads to memory loss progressively. It is a fatal brain disease that mostly affects the elderly. It steers the decline of cognitive and biological functions of the brain and shrinks the brain successively, which in turn is known as Atrophy. For an accurate diagnosis of Alzheimer's disease, cutting-edge methods like machine learning are essential. Recently, machine learning has gained a lot of attention and popularity in the medical industry. As the illness progresses, those with Alzheimer's have a far more difficult time doing even the most basic tasks, and in the worst case, their brain completely stops functioning. A person's likelihood of having early-stage Alzheimer's disease may be determined using the ML method. In this analysis, papers on Alzheimer’s disease diagnosis based on deep learning techniques and reinforcement learning between 2008 and 2023 found in google scholar were studied—sixty relevant papers obtained after the search was considered for this study. These papers were analysed based on the biomarkers of AD and the machine-learning techniques used. The analysis shows that deep learning methods have an immense ability to extract features and classify AD with good accuracy. The DRL methods have not been used much in the field of image processing. The comparison results of deep learning and reinforcement learning illustrate that the scope of Deep Reinforcement Learning (DRL) in dementia detection needs to be explored.

Keywords
Alzheimer’s Disease, Deep Learning, Convolutional Neural Network, Recurrent Neural Network, Deep Neural Network.

References
[1] “2021 Alzheimer’s Disease Facts and Figures,” Alzheimers Dement, vol. 17, no. 3, pp. 327-406, 2021. Google Scholar | CrossRef | Publisher Link
[2] Stefan Klöppel et al., “Automatic Classification of MR Scans in Alzheimer’s Disease,” Brain, vol. 131, no. 3, pp. 681-9, 2008. Google Scholar | CrossRef | Publisher Link
[3] Guy M McKhann et al., “The Diagnosis of Dementia Due to Alzheimer’s Disease: Recommendations From the National Institute on Aging-Alzheimer’s Association Workgroups on Diagnostic Guidelines for Alzheimer’s Disease,” Alzheimer’s Dement, vol. 7, no. 3, pp. 263-9, 2011. Google Scholar | CrossRef | Publisher Link
[4] Maria C Carrillo et al., “Revisiting the Framework of the National Institute on Aging-Alzheimer’s Association Diagnostic Criteria,” Alzheimers Dement, vol. 9, no. 5, pp. 594-601, 2013. Google Scholar | CrossRef | Publisher Link
[5] Elif Eyigoz et al., “Linguistic Markers Predict the Onset of Alzheimer’s Disease,” EclinicalMedicine, vol. 28, 2020. Google Scholar | CrossRef | Publisher Link
[6] Elena Ryzhikova et al., “Raman Spectroscopy and Machine Learning for Biomedical Applications: Alzheimer’s Disease Diagnosis Based on the Analysis of Cerebrospinal Fluid,” Spectrochimica Acta Part A: Molecular and Biomolecular Spectroscopy, vol. 248, 2021. Google Scholar | CrossRef | Publisher Link
[7] Taesic Lee, and Hyunju Lee, “Prediction of Alzheimer’s Disease Using Blood Gene Expression Data,” Science Report, vol. 10, no. 1, p. 3485, 2020. Google Scholar | CrossRef | Publisher Link
[8] Karl Herrup, “Commentary on ‘Recommendations From the National Institute on Aging-Alzheimer’s Association Workgroups on Diagnostic Guidelines for Alzheimer’s Disease,” Addressing the Challenge of Alzheimer’s Disease in the 21st Century,” Alzheimer’s and Dementia, vol. 7, no. 3, pp. 335-7, 2011. CrossRef | Publisher Link
[9] Ece Bayram et al., “Current Understanding of Magnetic Resonance Imaging Biomarkers and Memory in Alzheimer’s Disease,” Alzheimer’s and Dementia: Translational Research and Clinical Interventions, vol. 14, no. 4, pp. 395-413, 2018. Google Scholar | CrossRef | Publisher Link
[10] Stefan J Teipel et al., “Comprehensive Dissection of the Medial Temporal Lobe in AD: Measurement of the Hippocampus, Amygdala, Entorhinal, Perirhinal and Parahippocampal Cortices Using MRI,” Journal of Neurology, vol. 253, no. 6, pp. 794-800, 2006. Google Scholar | CrossRef | Publisher Link
[11] Margarida Silveira, and Jorge Marques, “Boosting Alzheimer Disease Diagnosis Using PET Images,” 20th International Conference on Pattern Recognition, 2010. Google Scholar | CrossRef | Publisher Link
[12] Esther E. Bron et al., “Feature Selection Based on the SVM Weight Vector for Classification of Dementia,” IEEE Journal of Biomedical and Health Informatics, vol. 19, no. 5, pp. 1617–1626, 2015. Google Scholar | CrossRef | Publisher Link
[13] Ling Wang et al., “Region-of-Interest Based Sparse Feature Learning Method for Alzheimer’s Disease Identification,” Computer Methods and Programs in Biomedicine, vol. 187, 2020. Google Scholar | CrossRef | Publisher Link
[14] M. Latha, and S. Arun, “Detection of Roi for Classifying Alzheimer’s Disease Using Mr. Image of Brain,” International Journal of Innovative Technology and Exploring Engineering, vol. 8, no. 5, pp. 2278–3075, 2019. Publisher Link
[15] Xianglian Meng et al., “Research on Voxel-Based Features Detection and Analysis of Alzheimer’s Disease Using Random Survey Support Vector Machine,” Front Neuroinformatics, vol. 16, p. 15, 2022. Google Scholar | CrossRef | Publisher Link
[16] Feng Zhang et al., “Voxel-Based Morphometry: Improving the Diagnosis of Alzheimer’s Disease Based on an Extreme Learning Machine Method From the ADNI Cohort,” Neuroscience, vol. 414, pp. 273–279, 2019. Google Scholar | CrossRef | Publisher Link
[17] Eunho Lee et al., “Toward an Interpretable Alzheimer’s Disease Diagnostic Model With Regional Abnormality Representation Via Deep Learning,” NeuroImage, vol. 202, p. 116113, 2019. Google Scholar | CrossRef | Publisher Link
[18] Wenjie Kang et al., “Multi-Model and Multi-Slice Ensemble Learning Architecture Based on 2D Convolutional Neural Networks for Alzheimer’s Disease Diagnosis,” Computers in Biology and Medicine, vol. 136, 2021. Google Scholar | CrossRef | Publisher Link
[19] Amir Ebrahimi-Ghahnavieh et al., “Transfer Learning for Alzheimer’s Disease Detection on MRI Images,” IEEE International Conference on Industry 4.0, Artificial Intelligence, and Communications Technology, pp. 133–138, 2019. Google Scholar | CrossRef | Publisher Link
[20] Yanteng Zhang et al., “Diagnosis of Alzheimer’s Disease Based on Regional Attention with Smri Gray Matter Slices,” Journal of Neuroscience Methods, vol. 365, p. 109376, 2022. Google Scholar | CrossRef | Publisher Link
[21] Fan Li, and Manhua. Liu, “Alzheimer’s Disease Diagnosis Based on Multiple Cluster Dense Convolutional Networks,” Computerized Medical Imaging and Graphics, vol. 70, pp. 101–110, 2018. Google Scholar | CrossRef | Publisher Link
[22] Manhua Liu et al., “Ensemble Sparse Classification of Alzheimer’s Disease,” NeuroImage, vol. 60, no. 2, pp. 1106–1116, 2012. Google Scholar | CrossRef | Publisher Link
[23] Ruhul Amin Hazarika et al., “An Improved LeNet-Deep Neural Network Model for Alzheimer’s Disease Classification Using Brain Magnetic Resonance Images,” IEEE Access, vol. 9, 2021. Google Scholar | CrossRef | Publisher Link
[24] S. Soundarya et al., “Early Detection of Alzheimer Disease Using Gadolinium Material,” Materials Today: Proceedings, vol. 45, 2021. Google Scholar | CrossRef | Publisher Link
[25] Nicola Amoroso et al., “Deep Learning Reveals Alzheimer’s Disease Onset in MCI Subjects: Results From an International Challenge,” Journal of Neuroscience Methods, vol. 302, pp. 3–9, 2018. Google Scholar | CrossRef | Publisher Link
[26] Michael Eickenberg, “Seeing It All: Convolutional Network Layers Map the Function of the Human Visual System,” NeuroImage, vol. 152, pp. 184–194, 2017. Google Scholar | CrossRef | Publisher Link
[27] Xiaoxiao Chen et al., “The Application of Convolutional Neural Network Model in Diagnosis and Nursing of MR Imaging in Alzheimer’s Disease,” Interdisciplinary Sciences: Computational Life Sciences, vol. 14, no. 1, pp. 34-44, 2022. Google Scholar | CrossRef | Publisher Link
[28] Jyoti Islam, and Yanqing Zhang, “Brain MRI Analysis for Alzheimer’s Disease Diagnosis Using an Ensemble System of Deep Convolutional Neural Networks,” Brain Informatics, vol. 5, no. 2, 2018. Google Scholar | CrossRef | Publisher Link
[29] Mohammed Abdelaziz, Tianfu Wang, and Ahmed Elazab, “Alzheimer’s Disease Diagnosis Framework from Incomplete Multimodal Data Using Convolutional Neural Networks,” Journal of Biomedical Informatics, vol. 121, 2021. Google Scholar | CrossRef | Publisher Link
[30] Jianping Qiao et al., “Multivariate Deep Learning Classification of Alzheimer’s Disease Based on Hierarchical Partner Matching Independent Component Analysis) Multivariate Deep Learning Classification of Alzheimer’s Disease Based on Hierarchical Partner Matching Independent Component Analysis. Front,” Frontiers in Aging Neuroscience, vol. 10, p. 417, 2018. Google Scholar | CrossRef | Publisher Link
[31] V .P. Amadi, N.D Nwiabu, and V. I. E. Anireh, "Case-Based Reasoning System for the Diagnosis and Treatment of Breast, Cervical and Prostate Cancer," SSRG International Journal of Computer Science and Engineering , vol. 8, no. 8, pp. 13-20, 2021. Google Scholar | CrossRef | Publisher Link
[32] Mohit Dua et al., “A CNN–RNN–LSTM Based Amalgamation for Alzheimer’s Disease Detection,” Journal of Medical and Biological Engineering, vol. 40, no. 5, 2020. Google Scholar | CrossRef | Publisher Link
[33] Minh Nguyen et al., “Predicting Alzheimer’s Disease Progression Using Deep Recurrent Neural Networks,” NeuroImage, vol. 222, 2020. Google Scholar | CrossRef | Publisher Link
[34] Ruoxuan Cui, and Manhua Liu, “RNN-Based Longitudinal Analysis for Diagnosis of Alzheimer’s Disease,” Computerized Medical Imaging and Graphics, vol. 73, pp. 1–10, 2019. Google Scholar | CrossRef | Publisher Link
[35] F. Li and M. Liu, “A Hybrid Convolutional and Recurrent Neural Network for Hippocampus Analysis in Alzheimer’s Disease,” Journal of Neuroscience Methods, vol. 323, pp. 108-118, 2019. Google Scholar | CrossRef | Publisher Link
[36] Tingyan Wang, Robin G. Qiu, and Ming Yu, “Predictive Modeling of the Progression of Alzheimer’s Disease with Recurrent Neural Networks,” Scientific Report, vol. 8, no. 1, 2018. Google Scholar | CrossRef | Publisher Link
[37] Yi-Wei Chien et al., “An Automatic Assessment System for Alzheimer’s Disease Based on Speech Using Feature Sequence Generator and Recurrent Neural Network,” Scientific Report, vol. 9, no. 1, 2019. Google Scholar | CrossRef | Publisher Link
[38] Feng Li et al., “A Robust Deep Model for Improved Classification of AD/MCI Patients,” IEEE Journal of Biomedical and Health Informatics, vol. 19, no. 5, pp. 1610–1616, 2015. Google Scholar | CrossRef | Publisher Link
[39] Moh. Faturrahman et al., “Structural MRI Classification for Alzheimer’s Disease Detection Using Deep Belief Network,” 11th International Conference on Information & Communication Technology and System, pp. 37–42, 2018. Google Scholar | CrossRef | Publisher Link
[40] Heung-Il Suk et al., “Hierarchical Feature Representation and Multimodal Fusion with Deep Learning for AD/MCI Diagnosis,” NeuroImage, vol. 101, pp. 569–582, 2014. Google Scholar | CrossRef | Publisher Link
[41] Afreen Khan, and Swaleha Zubair, “An Improved Multi-Modal Based Machine Learning Approach for the Prognosis of Alzheimer’s Disease,” Journal of King Saud University-Computer and Information Sciences, vol. 34, no. 6, pp. 2688-2706, 2022. Google Scholar | CrossRef | Publisher Link
[42] Nguyen Thanh Duc et al., “3D-Deep Learning Based Automatic Diagnosis of Alzheimer’s Disease with Joint MMSE Prediction Using Resting-State fMRI,” Neuroinformatics, vol. 18, no. 1, pp. 71-86, 2020. Google Scholar | CrossRef | Publisher Link
[43] Elaheh Moradi et al., “Machine Learning Framework for Early MRI-Based Alzheimer's Conversion Prediction in MCI Subjects,” Neuroimage, vol. 104, pp. 398-412, 2015. Google Scholar | CrossRef | Publisher Link
[44] Tran Anh Tuan et al., “Alzheimer’s Diagnosis Using Deep Learning in Segmenting and Classifying 3D Brain MR Images,” International Journal of Neuroscience, vol. 132, no. 7, pp. 689-698. Google Scholar | CrossRef | Publisher Link
[45] Martin Dyrba et al., “Robust Automated Detection of Microstructural White Matter Degeneration in Alzheimer’s Disease Using Machine Learning Classification of Multicenter DTI Data,” Plos One, vol. 8, no. 5, p. E64925, 2013. Google Scholar | CrossRef | Publisher Link
[46] Zhao Fan et al., “Classification of Alzheimer’s Disease Based on Brain MRI and Machine Learning,” Neural Computing and Applications, vol. 32, no. 7, pp. 1927-1936, 2020. Google Scholar | CrossRef | Publisher Link
[47] Juan Zhou et al., “A Correlation Analysis Between Snps and Rois of Alzheimer’s Disease Based on Deep Learning,” Biomed Research International, vol. 2021, pp. 1-13, 2021. Google Scholar | CrossRef | Publisher Link
[48] Vaibhav Gupta, and Dr. Pallavi Murghai Goel, "Heart Disease Prediction Using ML," SSRG International Journal of Computer Science and Engineering, vol. 7, no. 6, pp. 17-19, 2020. CrossRef | Publisher Link
[49] Shuang Liang, and Yu Gu, “Computer-Aided Diagnosis of Alzheimer’s Disease Through Weak Supervision Deep Learning Framework With An Attention Mechanism,” Sensors, vol. 21, no. 1, p. 220, 2020. Google Scholar | CrossRef | Publisher Link
[50] Junxiu Liu et al., “Alzheimer's Disease Detection Using Depthwise Separable Convolutional Neural Networks,” Computer Methods and Programs in Biomedicine, vol. 203, p. 106032, 2021. Google Scholar | CrossRef | Publisher Link
[51] Chenhui Hu et al., “Clinical Decision Support for Alzheimer's Disease Based on Deep Learning and Brain Network,” 2016 IEEE International Conference on Communications, pp. 1-6, 2016. Google Scholar | CrossRef | Publisher Link
[52] Bin Lu et al., “A Practical Alzheimer’s Disease Classifier Via Brain Imaging-Based Deep Learning on 85,721 Samples,” Journal of Big Data, vol. 9, no. 1, pp. 1-22, 2022. Google Scholar | CrossRef | Publisher Link
[53] Firouzeh Razavi, Mohammad Jafar Tarokh , and Mahmood Alborzi, “An Intelligent Alzheimer’s Disease Diagnosis Method Using Unsupervised Feature Learning,” Journal of Big Data, vol. 6, no. 1, pp. 1-16, 2019. Google Scholar | CrossRef | Publisher Link
[54] Serkan Savaş, “Detecting the Stages of Alzheimer’s Disease with Pre-Trained Deep Learning Architectures,” Arabian Journal for Science and Engineering, vol. 47, no. 2, pp. 2201-2218, 2022. Google Scholar | CrossRef | Publisher Link
[55] Yiming Ding et al., “A Deep Learning Model to Predict a Diagnosis of Alzheimer Disease By Using 18F-FDG PET of the Brain,” Radiology, vol. 290, no. 2, pp. 456-464, 2018. Google Scholar | CrossRef | Publisher Link
[56] Rosalba Gaudiuso et al., “Diagnosis of Alzheimer's Disease Using Laser-Induced Breakdown Spectroscopy and Machine Learning,” Spectrochimica Acta Part B: Atomic Spectroscopy, vol. 171, p. 105931, 2020. Google Scholar | CrossRef | Publisher Link
[57] Tian Jipeng, Suma P., and Dr. T.C.Manjunath, "AI, ML and the Eye Disease Detection," SSRG International Journal of Computer Science and Engineering, vol. 7, no. 4, pp. 1-3, 2020. CrossRef | Publisher Link
[58] Jianqiao Tian et al., “Modular Machine Learning for Alzheimer's Disease Classification From Retinal Vasculature,” Scientific Reports, vol. 11, no. 1, pp. 1-11. Google Scholar | CrossRef | Publisher Link
[59] Patricio Andres Donnelly-Kehoe et al., “Looking for Alzheimer's Disease Morphometric Signatures Using Machine Learning Techniques,” Journal of Neuroscience Methods, vol. 302, pp. 24-34, 2018. Google Scholar | CrossRef | Publisher Link
[60] Shunsuke Koga et al., “Deep Learning-Based Model for Diagnosing Alzheimer's Disease and Tauopathies,” Neuropathology and Applied Neurobiology, vol. 48, no. 1, p. E12759, 2022. Google Scholar | CrossRef | Publisher Link
[61] Arshad Hashmi, and Omar Barukab, “Dementia Classification Using Deep Reinforcement Learning for Early Diagnosis,” Applied Sciences, vol. 13, no. 3, 2023. Google Scholar | CrossRef | Publisher Link
[62] Krishnakant V. Saboo et al., “Reinforcement Learning Based Disease Progression Model for Alzheimer’s Disease,” 35th Conference on Neural Information Processing Systems (NeurIPS 2021), pp. 20903-20915, 2021. Google Scholar | Publisher Link
[63] Rongjian Li et al., “Deep Learning Based Imaging Data Completion for Improved Brain Disease Diagnosis,” Medical Image Computing and Computer Assisted Intervention, vol. 8675, 2014. Google Scholar | CrossRef | Publisher Link
[64] Zhen Pang et al., “A Multi-Modal Data Platform for Diagnosis and Prediction of Alzheimer’s Disease Using Machine Learning Methods,” Mobile Networks and Applications, vol. 26, no. 6, pp. 2341-2352, 2021. Google Scholar | CrossRef | Publisher Link
[65] Muhammad Hammad Memon, “Early Stage Alzheimer’s Disease Diagnosis Method,” 2019 16th International Computer Conference on Wavelet Active Media Technology and Information Processing, pp. 222-225, 2019. Google Scholar | CrossRef | Publisher Link