A Comparison Study of the Performance of Machine Learning Models for Alzheimer’s Disease Classification
Article Sidebar
Main Article Content
Abstract
This research studied and compared the performance of machine learning models for Alzheimer’s disease classification. Alzheimer ‘s is the most founded in the disease of the brain. Moreover, it affects the daily life of people. From much research indicated that there are about 55 million people in the world that have Dementia disease, 60 to 70 percent of them are caused Alzheimer’s disease. Researcher studied the classification for urgent on the way of treatment. Darwin dataset is used to compare in this study. There are 451 covariates and 174 observations. Python is used in this study. Moreover, those covariates are multicollinearity and high dimensional dataset. The Principal Component Analysis is the technique for dealing first and then the supervise machine learning for classification on the outcome of Alzheimer. Those methods to compare contain Random Forest, logistic regression and XGBoost. The results pointed that logistic regression yields the high performance for classification. The Accuracy is 88.57%, precision is 100%, recall is 80.00% and F1-score is 88.89%, respectively.
Article Details
This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.
The content within the published articles, including images and tables, is copyrighted by Rajamangala University of Technology Rattanakosin. Any use of the article's content, text, ideas, images, or tables for commercial purposes in various formats requires permission from the journal's editorial board.
Rajamangala University of Technology Rattanakosin permits the use and dissemination of article files under the condition that proper attribution to the journal is provided and the content is not used for commercial purposes.
The opinions and views expressed in the articles are solely those of the respective authors and are not associated with Rajamangala University of Technology Rattanakosin or other faculty members in the university. The authors bear full responsibility for the content of their articles, including any errors, and are responsible for the content and editorial review. The editorial board is not responsible for the content or views expressed in the articles.
References
ลักขณา อินทร์กลับ. (2557). เข้าใจและเข้าถึงผู้ป่วยโรคอัลไซเมอร์. วารสารมหาวิทยาลัยคริสเตียน. 20(3), 439-446.
Matthews, K. A., Xu, W., Gaglioti, A. H., Holt, J.B., Croft, J. B., Mack, D., & McGuire, L. C. (2019). Racial and ethnic estimates of Alzheimer’s disease and related dementias in the United States (2015-2060) in adults aged≥ 65 years. Alzheimer’s & Dementia. 15(1), 17-24. https://doi.org/10.1016/j.jalz.2018.06.3063
GÜNDOĞDU, S. (2022). Hepatitis C Disease Detection Based on PCA–SVM Model. Hittite Journal of Science and Engineering. 9(2), 111-116. https://doi.org/10.17350/HJSE19030000261
Mamdouh Farghaly, H., Shams, M. Y., & Abd El-Hafeez, T. (2023). Hepatitis C Virus prediction based on machine learning framework: a real-world case study in Egypt. Knowledge and Information Systems. 65, 2595-2617. 10.1007/s10115-023-01851-4
Uddin, S., Khan, A., Hossain, M. E., & Moni, M. A. (2019). Comparing different supervised machine learning algorithms for disease prediction. BMC medical informatics and decision making. 19(1), 1-16. 10.1186/s12911-019-1004-8
Nagavelli, U., Samanta, D., & Chakraborty, P. (2022). Machine learning technology-based heart disease detection models. Journal of Healthcare Engineering. 1-9. https://doi.org/10.1155/2022/7351061
Meriç, E., & Özer, Ç. (2022). Symptom Based Health Status Prediction via Decision Tree, KNN, XGBoost, LDA, SVM, and Random Forest. Proceedings of International Conference on Computing Intelligence and Data Analytics, Koceli, Turkey, 193-207.
Powers, D. M. (2008). Evaluation From Precision, Recall and F-Factor to ROC, Informedness, Markedness & Correlation. Internation Journal of Machine Learning Technology. 2(1), 37-63. https://doi.org/10.48550/arXiv.2010.16061
Asif, M. A. A. R., Nishat, M. M., Faisal, F., Dip, R. R., Udoy, M. H., Shikder, M. F., & Ahsan, R. (2021). Performance Evaluation and Comparative Analysis of Different Machine Learning Algorithms in Predicting Cardiovascular Disease. Engineering Letters. 29(2), 731-741.
Syafa’ah, L., Zulfatman, Z., Pakaya, I., & Lestandy, M. (2021). Comparison of machine learning classification methods in hepatitis C virus. Journal Online Informatika. 6(1), 73-78. 10.15575/join.v6i1.719
Cilia, N. D., De Gregorio, G., De Stefano, C., Fontanella, F., Marcelli, A., & Parziale, A. (2022). Diagnosing Alzheimer’s disease from on-line handwriting: a novel dataset and performance benchmarking. Engineering Applications of Artificial Intelligence. 111, 1-12.
https://doi.org/10.1016/j.engappai.2022.104822
Mukherjee, S., Niu, Z., Halder, S. Bhattacharya, B. B., & Michailidis, G. (2024). High Dimensional Logistic Regression Under Network Dependence. https://doi.org/10.48550/arXiv.2110.03200
