Abstract
Alzheimer’s disease (AD) is a kind of common disease among the elderly which can affect function of cognitive abilities. Machine learning (ML) has been applied to the prediction of AD recently, but due to the limitations of single modal data, its performance still can be improved. In this study, we totally downloaded 216 data of patients for three groups: AD, mild cognitive impairment (MCI) and normal control (NC). Using the joint feature set of brain cortical characteristics along with biology, risk factors, PET measures and cognitive scores, four machine learning algorithms were applied to predict the mini-mental state examination (MMSE) scores of patients at sixth month (M06) and one year later (M12). The best performance of mean absolute error (MAE), mean squared error (MSE) and root mean squared error (RMSE) reached 1.400, 2.313, 5.307, respectively. Compared with previous researches, our MRI-based joint feature set showed valuable capability in the task of MMSE scores regression.
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References
Sharma, R.: FAF-DRVFL: Fuzzy activation function based deep random vector functional links network for early diagnosis of Alzheimer disease. Applied Soft Comput. 106 (2021)
Liu, S., Cai, W.: Early diagnosis of Alzheimer’s disease with deep learning. In: IEEE 11th International Symposium on Biomedical Imaging (ISBI), pp. 1015–1018 (2014)
Qiao, H.: Ranking convolutional neural network for Alzheimer’s disease mini-mental state examination prediction at multiple time points. Computer Methods and Programs in Biomedicine 213, 106503 (2022)
Stonnington, C.M.: Predicting clinical scores from magnetic resonance scans in Alzheimer’s disease. Neuroimage 51(4), 05–13 (2010)
Daoqiang, Z.: Predicting future clinical changes of MCI patients using longitudinal and multimodal biomarkers. PLoS ONE 7(3), e33182 (2012)
Baiying, L.: Predicting clinical scores for Alzheimer’s disease based on joint and deep learning. Expert Syst. Appl. 187(11), 59–66 (2022)
Huang, D.S.: Radial basis probabilistic neural networks: model and application. Int. J. Pattern Recognition and Artificial Intelligence 13(7), 1083–1101 (1999)
Breiman, L.: Random forests. Mach. Learn. 45(1), 157–176 (2001)
HaiJun, F.: Estimation of solubility of acid gases in ionic liquids using different machine learning methods. Journal of Molecular Liquids 349, 118413 (2022)
Breiman, L.: Bagging Predictors. Machine Learn 24(2), 123–140 (1996)
Mengya, Y.: Joint and deep ensemble regression of clinical scores of Alzheimer’s disease using longitudinal and incomplete and data. In: Annual International Conference of the IEEE Engineering in Medicine and Biology Society, pp. 1254–1257 (2018)
Acknowledgements
This study was funded by the National Key Research Development Program of China (2020YFC2008700) and the grants of National Natural Science Foundation of China (Nos 61971275, 81830052 and 82072228).
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Luo, S., Yao, X., Yuan, Z., Zhou, L. (2023). Prediction of Mini-mental State Examination Scores via Machine Learning for Alzheimer’s Disease. In: You, P., Li, H., Chen, Z. (eds) Proceedings of International Conference on Image, Vision and Intelligent Systems 2022 (ICIVIS 2022). ICIVIS 2022. Lecture Notes in Electrical Engineering, vol 1019. Springer, Singapore. https://doi.org/10.1007/978-981-99-0923-0_87
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DOI: https://doi.org/10.1007/978-981-99-0923-0_87
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