Authors : Abdul Samad; Enes Samet Aydı

Volume/Issue : Volume 9 - 2024, Issue 6 - June

Google Scholar : https://tinyurl.com/mupx6sbc

Scribd : https://tinyurl.com/mrnmhsma

DOI : https://doi.org/10.38124/ijisrt/IJISRT24JUN1915

Note : A published paper may take 4-5 working days from the publication date to appear in PlumX Metrics, Semantic Scholar, and ResearchGate.

Abstract : Alzheimer's disease (AD) is a leading cause of dementia, predominantly impacting the elderly and characterized by progressive cognitive decline. Early and precise detection is critical for effective management and improved patient outcomes. Traditional diagnostic methods such as neuroimaging and cerebrospinal fluid analysis are often invasive, expensive, and time- consuming. Advances in artificial intelligence (AI) and machine learning (ML) provide promising alternatives that are non-invasive, efficient, and cost-effective. This study explores the application of various ML algorithms to predict Alzheimer's disease. The methodology involved data preprocessing and feature selection using the Spearman algorithm to enhance computational efficiency and model performance. We evaluated k-Nearest Neighbors (k-NN), Naive Bayes (NB), Decision Trees (DT), and Ensemble methods. Results indicate that the Ensemble method achieved a predictive accuracy of 94.07% using only 13 features. These results demonstrate the potential of ML algorithms in revolutionizing AD diagnostics, offering scalable and accurate solutions for early detection.

Keywords : Alzheimer's Disease; Early Prediction; Machine Learning; Artificial Intelligence; Feature Selection; Predictive Accuracy.

References :

1. A. P. Porsteinsson, R. S. Isaacson, S. Knox, M. N. Sabbagh, and I. Rubino, “Diagnosis of Early Alzheimer’s Disease: Clinical Practice in 2021,” Journal of Prevention of Alzheimer’s Disease, vol. 8, no. 3, pp. 371–386, Jul. 2021, doi: 10.14283/JPAD.2021.23/TABLES/3.
2. R. Mayeux and Y. Stern, “Epidemiology of Alzheimer Disease,” Cold Spring Harb Perspect Med, vol. 2, no. 8, 2012, doi: 10.1101/CSHPERSPECT.A006239.
3. C. R. Jack et al., “NIA-AA Research Framework: Toward a biological definition of Alzheimer’s disease,” Alzheimers Dement, vol. 14, no. 4, pp. 535–562, Apr. 2018, doi: 10.1016/J.JALZ.2018.02.018.
4. B. Dubois et al., “Preclinical Alzheimer’s disease: Definition, natural history, and diagnostic criteria,” Alzheimers Dement, vol. 12, no. 3, pp. 292–323, Mar. 2016, doi: 10.1016/J.JALZ.2016.02.002.
5. S. Rathore, M. Habes, M. A. Iftikhar, A. Shacklett, and C. Davatzikos, “A review on neuroimaging-based classification studies and associated feature extraction methods for Alzheimer’s disease and its prodromal stages,” Neuroimage, vol. 155, p. 530, Jul. 2017, doi: 10.1016/J.NEUROIMAGE.2017.03.057.
6. S. Klöppel et al., “Automatic classification of MR scans in Alzheimer’s disease,” Brain, vol. 131, no. Pt 3, pp. 681–689, Mar. 2008, doi: 10.1093/BRAIN/AWM319.
7. C. Kavitha, V. Mani, S. R. Srividhya, O. I. Khalaf, and C. A. Tavera Romero, “Early-Stage Alzheimer’s Disease Prediction Using Machine Learning Models,” Front Public Health, vol. 10, Mar. 2022, doi: 10.3389/FPUBH.2022.853294.
8. Q. Li et al., “Early prediction of Alzheimer’s disease and related dementias using real-world electronic health records,” Alzheimer’s and Dementia, vol. 19, no. 8, pp. 3506–3518, Aug. 2023, doi: 10.1002/ALZ.12967.
9. F. J. Martinez-Murcia, A. Ortiz, J. M. Gorriz, J. Ramirez, and D. Castillo-Barnes, “Studying the Manifold Structure of Alzheimer’s Disease: A Deep Learning Approach Using Convolutional Autoencoders,” IEEE J Biomed Health Inform, vol. 24, no. 1, pp. 17–26, Jan. 2020, doi: 10.1109/JBHI.2019.2914970.
10. R. Prajapati, U. Khatri, and G. R. Kwon, “An Efficient Deep Neural Network Binary Classifier for Alzheimer’s Disease Classification,” Digital Signal Processing and Signal Processing Education Workshop, pp. 231–234, Apr. 2021, doi: 10.1109/ICAIIC51459.2021.9415212.
11. H. A. Helaly, M. Badawy, and A. Y. Haikal, “Deep Learning Approach for Early Detection of Alzheimer’s Disease,” Cognit Comput, vol. 14, no. 5, pp. 1711–1727, Sep. 2022, doi: 10.1007/S12559-021-09946-2/FIGURES/15.
12. M. Liu, D. Zhang, and D. Shen, “Ensemble sparse classification of Alzheimer’s disease,” Neuroimage, vol. 60, no. 2, pp. 1106–1116, Apr. 2012, doi: 10.1016/J.NEUROIMAGE.2012.01.055.
13. M. Nour, D. Kandaz, M. Kursad Ucar, K. Polat, and A. Alhudhaif, “Machine Learning and Electrocardiography Signal-Based Minimum Calculation Time Detection for Blood Pressure Detection,” 2022, doi: 10.1155/2022/5714454.
14. RABIE EL KHAROUA, “Alzheimer’s Disease Dataset.” Accessed: Jun. 29, 2024. [Online]. Available: https://www.kaggle.com/datasets/rabieelkharoua/alzheimers-disease-dataset
15. A. Samad and M. Kürsad, “Enhancing Milk Quality Detection with Machine Learning: A Comparative Analysis of KNN and Distance-Weighted KNN Algorithms”, doi: 10.38124/ijisrt/IJISRT24MAR2123.
16. H. Chen, S. Hu, R. Hua, and X. Zhao, “Improved naive Bayes classification algorithm for traffic risk management,” EURASIP J Adv Signal Process, vol. 2021, no. 1, Dec. 2021, doi: 10.1186/S13634-021-00742-6.
17. C. Kaun, N. Z. Jhanjhi, W. W. Goh, and S. Sukumaran, “Implementation of Decision Tree Algorithm to Classify Knowledge Quality in a Knowledge Intensive System,” MATEC Web of Conferences, vol. 335, p. 04002, 2021, doi: 10.1051/MATECCONF/ 202133504002.
18. F. Huang, G. Xie, and R. Xiao, “Research on ensemble learning,” 2009 International Conference on Artificial Intelligence and Computational Intelligence, AICI 2009, vol. 3, pp. 249–252, 2009, doi: 10.1109/AICI.2009.235.