Alzheimer’s Disease Based on Machine Learning Algorithms and Mind Maps: Review
Keywords:
Machin-Learning Algorithms, Dementia and Alzheimer’s Classifications, Mind maps modelsAbstract
Alzheimer's disease (AD) is a complex neurological illness that has several deep reasons. According to recent research, the use of machine learning techniques (ML) on MRI images can assist in identifying the brain regions and the connections between them that are implicated in dementia. The study aims to review literature from 2017 to 2023 on the use of machine learning algorithms to identify and categorize AD. The precision of each machine learning model is assessed, and mind map models are employed to illustrate the study and compare the outcomes
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References
[1] M. Lohar and R. Patange, "A survey on classification methods of brain MRI for Alzheimer’s disease," Int. J.
Eng. Res. Technol, vol. 7, pp. 339-348, 2018.
[2] N. B. Balakrishnan, P. Sreeja, and J. J. Panackal, "Alzheimers Disease Diagnosis using Machine Learning: A
Review," arXiv preprint arXiv:2304.09178, 2023 Pereira, "Alzheimer's disease: the quest to understand complexity," Journal of Alzheimer's disease, vol. 21,
pp. 373-383, 2010.
[4] W. M. van der Flier, Y. A. Pijnenburg, N. Prins, A. W. Lemstra, F. H. Bouwman, C. E. Teunissen, B. N. van
Berckel, C. J. Stam, F. Barkhof, and P. J. Visser, "Optimizing patient care and research: the Amsterdam
Dementia Cohort," Journal of Alzheimer's disease, vol. 41, pp. 313-327, 2014.
[5] M. R. Ahmed, Y. Zhang, Z. Feng, B. Lo, O. T. Inan, and H. Liao, "Neuroimaging and machine learning for
dementia diagnosis: recent advancements and future prospects," IEEE reviews in biomedical engineering,
vol. 12, pp. 19-33, 2018.
[6] R. Bin-Hezam and T. E. Ward, "A machine learning approach towards detecting dementia based on its
modifiable risk factors," International Journal of Advanced Computer Science and Applications, vol. 10,
2019.
[7] B. Mahesh, "Machine learning algorithms-a review," International Journal of Science and Research
(IJSR).[Internet], vol. 9, pp. 381-386, 2020.
[8] F. J. M. Shamrat, S. Akter, S. Azam, A. Karim, P. Ghosh, Z. Tasnim, K. M. Hasib, F. De Boer, and K.
Ahmed, "AlzheimerNet: An effective deep learning based proposition for alzheimer’s disease stages
classification from functional brain changes in magnetic resonance images," IEEE Access, vol. 11, pp.
16376-16395, 2023.
[9] S. Gul, M. Asif, Z. Nawaz, M. H. Aziz, S. Khurram, M. Q. Saleem, E. O. A. Habib, M. Shafiq, and O. E.
Sheta, "Sustainable Learning of Computer Programming Languages Using Mind Mapping," Intelligent
Automation & Soft Computing, vol. 36, 2023.
10] Z. Zhao, J. H. Chuah, K. W. Lai, C.-O. Chow, M. Gochoo, S. Dhanalakshmi, N. Wang, W. Bao, and X. Wu,
"Conventional machine learning and deep learning in Alzheimer's disease diagnosis using neuroimaging: A review,"
Frontiers in Computational Neuroscience, vol. 17, p. 1038636, 2023.
[11] S. Kumari, K. Bagri, and R. Deshmukh, "Dementia: A journey from cause to cure," in Nanomedicine-Based
Approaches for the Treatment of Dementia, ed: Elsevier, 2023, pp. 37-56.
[12] I. A. Abdulmunem, "Brain MR Images Classification for Alzheimer’s Disease," Iraqi Journal of Science, pp.
2725-2740, 2022.
[13] R. K. Patel, E. Aggarwal, K. Solanki, O. Dahiya, and S. A. Yadav, "A Logistic Regression and Decision
Tree Based Hybrid Approach to Predict Alzheimer's Disease," in 2023 International Conference on
Computational Intelligence and Sustainable Engineering Solutions (CISES), 2023, pp. 722-726.
[14] A. P. Singh, N. Upadhyay, V. G. Shankar, and B. Devi, "IHDNA: Identical Hybrid Deep Neural Networks
for Alzheimer's Detection using MRI Dataset," in 2023 3rd International Conference on Intelligent
Communication and Computational Techniques (ICCT), 2023, pp. 1-7.
[15] H. Qiao, "Using K-Means Algorithm and Convolutional Neural Networks to Identify Alzheimer’s Disease in
Coronal Brain Scans," in Journal of Physics: Conference Series, 2021, p. 032050.
[16] A. A. A. El-Latif, S. A. Chelloug, M. Alabdulhafith, and M. Hammad, "Accurate Detection of Alzheimer’s
Disease Using Lightweight Deep Learning Model on MRI Data," Diagnostics, vol. 13, p. 1216, 2023.
[17] Y. Farouk and S. Rady, "Early diagnosis of alzheimer’s disease using unsupervised clustering," International
Journal of Intelligent Computing and Information Sciences, vol. 20, pp. 112-124, 2020.
[18] E. Altinkaya, K. Polat, and B. Barakli, "Detection of Alzheimer’s disease and dementia states based on deep
learning from MRI images: a comprehensive review," Journal of the Institute of Electronics and Computer,
vol. 1, pp. 39-53, 2020.
[19] A. Chandra, R. Chakraborty, S. Nandi, and B. Porel, "Novel Method for Detection of Alzheimer’s Disease
using Gini Impurity based Decision Tree Model," in 2023 10th International Conference on Signal
Processing and Integrated Networks (SPIN), 2023, pp. 142-147.
[20] A. Sarica, A. Cerasa, and A. Quattrone, "Random forest algorithm for the classification of neuroimaging data
in Alzheimer's disease: a systematic review," Frontiers in aging neuroscience, vol. 9, p. 329, 2017.
[21] Y. Wang and C. Li, "Functional magnetic resonance imaging classification based on random forest algorithm
in Alzheimer's disease," in 2019 International Conference on Image and Video Processing, and Artificial
Intelligence, 2019, pp. 16-22.
[22] R. Kumari, S. Goel, and S. Das, "Using SVM for Alzheimer’s Disease detection from 3D T1MRI," in 2022
IEEE 21st Mediterranean Electrotechnical Conference (MELECON), 2022, pp. 600-604.
[23] A. Mahjabeen, M. R. Mia, F. Shariful, N. Faruqui, and I. Mahmud, "Early Prediction and Analysis of DTI
and MRI-Based Alzheimer’s Disease Through Machine Learning Techniques," in Proceedings of the Fourth
International Conference on Trends in Computational and Cognitive Engineering: TCCE 2022, 2023, pp. 3-
13.
[24] R. Cui, M. Liu, and A. s. D. N. Initiative, "RNN-based longitudinal analysis for diagnosis of Alzheimer’s
disease," Computerized Medical Imaging and Graphics, vol. 73, pp. 1-10, 2019
[25] A. Ebrahimi, S. Luo, and f. t. A. s. Disease Neuroimaging
Initiative, "Convolutional neural networks for Alzheimer’s disease detection on MRI images," Journal of
Medical Imaging, vol. 8, pp. 024503-024503, 2021.
[26] C. Kaur, T. Panda, S. Panda, A. R. M. Al Ansari, M. Nivetha, and B. K. Bala, "Utilizing the Random Forest
Algorithm to Enhance Alzheimer’s disease Diagnosis," in 2023 Third International Conference on Artificial
Intelligence and Smart Energy (ICAIS), 2023, pp. 1662-1667.
[27] S. Basaia, F. Agosta, L. Wagner, E. Canu, G. Magnani, R. Santangelo, M. Filippi, and A. s. D. N. Initiative,
"Automated classification of Alzheimer's disease and mild cognitive impairment using a single MRI and
deep neural networks," NeuroImage: Clinical, vol. 21, p. 101645, 2019.
[28] J. Kim, M. Lee, M. K. Lee, S.-M. Wang, N.-Y. Kim, D. W. Kang, Y. H. Um, H.-R. Na, Y. S. Woo, and C.
U. Lee, "Development of random forest algorithm based prediction model of alzheimer’s disease using
neurodegeneration pattern," Psychiatry Investigation, vol. 18, p. 69, 2021.
[29] R. Cavoretto and A. De Rossi, "Achieving accuracy and efficiency in spherical modelling of real data,"
Mathematical Methods in the Applied Sciences, vol. 37, pp. 1449-1459, 2014.
