Browse > Article
http://dx.doi.org/10.6109/jkiice.2021.25.12.1729

Dementia Prediction Model based on Gradient Boosting  

Lee, Taein (Psychology & Applied Artifical Intelligence, Sungkyunkwan University)
Oh, Hayoung (College of Computing and Informatics, Sungkyunkwan University)
Publication Information
Journal of the Korea Institute of Information and Communication Engineering / v.25, no.12, 2021 , pp. 1729-1738 More about this Journal
Abstract
Machine learning has a close relationship with cognitive psychology and brain science and is developing together. This paper analyzes the OASIS-3 dataset using machine learning techniques and proposes a model for predicting dementia. Dimensional reduction through PCA (Principal Component Analysis) is performed on the data quantifying the volume of each area among OASIS-3 data, and only important elements (features) are extracted and then various machine learning including gradient boosting and stacking Apply the models and compare the performance of each. Unlike previous studies, the proposed technique has a great differentiation because it uses not only the brain biometric data, but also basic information data such as the participant's gender and medical information data of the participant. In addition, it was shown that the proposed technique through various performance evaluations is a model that can better predict dementia by finding features that are more related to dementia among various numerical data.
Keywords
Machine learning; OASIS-3; Dementia; Gradient boosting(XGBoost); Stacking;
Citations & Related Records
연도 인용수 순위
  • Reference
1 T. F. Oltmanns and R. E. Emery, "Abnormal Psychology," eight eidition, PEARSON, 2015.
2 C. Lian, M. Liu, Y. Pan, and D. Shen, "Attention-Guided Hybrid Network for Dementia Diagnosis With Structural MR Images," IEEE transactions on cybernetics, pp. 1-20, 2020.
3 S. Buvari and K. Pettersson, "A Comparison on Image, Numerical and Hybrid based Deep Learning for Computer-aided AD Diagnostics," Degree Project In Technology, First Cycle, 15 Credits Stockholm, Sweden, vol. 1, pp. 1-20, 2020.
4 T. Chen and C. Guestrin, "XGBoost: A Scalable Tree Boosting System," KDD '16: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data, pp. 785-794, 2016.
5 P. J. LaMontagne, T. LS. Benzinger, J. C. Morris, K. Sarah, H. Russ, X. Chengjie, G. Elizabeth, H. Jason, M. K. Vlassenko, G. A. Raichle, E. Marcus, C. Carlos, and M. Daniel, "OASIS-3:Longitudinal Neuroimaging, Clinical, and Cognitive Dataset for Normal Aging and Alzheimer Disease," medRxiv, 2019.
6 A. C. Muller and S. Guido, "Introduction to Machine Learning with Python," O'Reilly, Hanbit, pp. 178-180, 2018.
7 A. Geron, "Hands-On Machine Learning with Scikit-Learn, Keras, and Tensorflow Concepts, Tools, and Techniques to Build Intelligent Systems," O'Reilly, pp. 191-202, 2017.
8 D. S. Marcus, T. H. Wang, J. Parker, J. G. Csernansky, J. C. Morris, and R. L. Buckner, "Open Access Series of Imaging Studies (OASIS): cross-sectional MRI data in young, middle aged, nondemented, and demented older adults," J. Cogn. Neurosci, vol. 19, no. 9, pp. 1498-1507, 2007.   DOI
9 Y. B. Lee, K. Yoo, J. H. Roh, W. J. Moon, and Y. Jeong, "Brain-State Extraction Algorithm Based on the State Transition(BEST): A Dynamic Functoinal Brain Network Analysis in fMRI Study," Brain Topography vol. 32, pp. 897-913, 2019.   DOI
10 S. E. Ryu, D. H. Shin, and K. Chung, "Prediction Model of Dementia Risk Based on XGBoost Using Derived Variable Extraction and Hyper Parameter Optimization," IEEE Access, vol. 8, pp. 177708-177720, 2020.   DOI
11 Morphometry Stats and Global Measure of Volume [Internet]. Available: https://surfer.nmr.mgh.harvard.edu/fswiki/MorphometryStats.
12 MaxAbsScaler [Internet]. Available: https://scikit-learn.org/stable/modules/generated/sklearn.preprocessing.MaxAbsScaler.html#sklearn.preprocessing.MaxAbsScaler.