Remote Parkinson's disease severity prediction based on causal game feature selection

被引:0
作者
Xue, Zaifa [1 ,2 ]
Lu, Huibin [1 ,2 ]
Zhang, Tao [1 ,2 ]
Guo, Xiaonan [1 ,2 ]
Gao, Le [1 ,2 ]
机构
[1] Yanshan Univ, Sch Informat Sci & Engn, Qinhuangdao, Peoples R China
[2] Hebei Key Lab Informat Transmiss & Signal Proc, Qinhuangdao, Peoples R China
基金
中国国家自然科学基金;
关键词
Feature selection; Causal graph; Cooperative game; Telemonitoring; Parkinson's disease; ABSOLUTE ERROR MAE; DISCOVERY; EFFICIENT; SHAPLEY; RMSE;
D O I
10.1016/j.eswa.2023.122690
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
Telemonitoring of Parkinson's disease has important implications for early diagnosis and treatment of patients. Most of the existing feature selection methods for remote prediction of PD severity are based on correlation and rarely consider causality, thus compromising the robustness of the model. Therefore, a causal game-based feature selection (CGFS) model is proposed for remote PD symptom severity assessment. Firstly, to address the challenge of small data size, the similar patient transfer strategy is designed to find data from source domain patients with conditions similar to those of the target patient. Secondly, the undirected equivalent greedy search method is employed to construct the causal graph between features and PD severity scores, and the robustness of the model is improved by selecting causal features. Then, to enhance the prediction accuracy, this paper utilizes the cooperative game approach Shapley value to evaluate the contribution of neighborhood nodes of the target value, and selects the features with causality and high contribution to form the final feature subset. Finally, the subset is input into the random forest to further enhance robustness and performance of the model. Experiments on Parkinson's telemonitoring dataset and the tapping dataset with different biomarkers show that the robustness of the feature subset selected by the CGFS model, and the prediction performance is better than advanced models compared. Therefore, the validity and universality of the CGFS method is demonstrated in remote PD severity prediction.
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页数:14
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共 51 条
  • [1] Score-based methods for learning Markov boundaries by searching in constrained spaces
    Acid, Silvia
    de Campos, Luis M.
    Fernandez, Moises
    [J]. DATA MINING AND KNOWLEDGE DISCOVERY, 2013, 26 (01) : 174 - 212
  • [2] Aliferis C F, 2003, AMIA Annu Symp Proc, P21
  • [3] Scaling up the Greedy Equivalence Search algorithm by constraining the search space of equivalence classes
    Alonso-Barba, Juan I.
    delaOssa, Luis
    Gamez, Jose A.
    Puerta, Jose M.
    [J]. INTERNATIONAL JOURNAL OF APPROXIMATE REASONING, 2013, 54 (04) : 429 - 451
  • [4] The mPower study, Parkinson disease mobile data collected using ResearchKit
    Bot, Brian M.
    Suver, Christine
    Neto, Elias Chaibub
    Kellen, Michael
    Klein, Arno
    Bare, Christopher
    Doerr, Megan
    Pratap, Abhishek
    Wilbanks, John
    Dorsey, E. Ray
    Friend, Stephen H.
    Trister, Andrew D.
    [J]. SCIENTIFIC DATA, 2016, 3
  • [5] Energy management in residential microgrid using model predictive control-based reinforcement learning and Shapley value
    Cai, Wenqi
    Kordabad, Arash Bahari
    Gros, Sebastien
    [J]. ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE, 2023, 119
  • [6] Root mean square error (RMSE) or mean absolute error (MAE)? - Arguments against avoiding RMSE in the literature
    Chai, T.
    Draxler, R. R.
    [J]. GEOSCIENTIFIC MODEL DEVELOPMENT, 2014, 7 (03) : 1247 - 1250
  • [7] Speech Based Estimation of Parkinson's Disease Using Gaussian Processes and Automatic Relevance Determination
    Despotovic, Vladimir
    Skovranek, Tomas
    Schommer, Christoph
    [J]. NEUROCOMPUTING, 2020, 401 (401) : 173 - 181
  • [8] Multiscale feature extraction from the perspective of graph for hob fault diagnosis using spectral graph wavelet transform combined with improved random forest
    Dong, Xin
    Li, Guolong
    Jia, Yachao
    Xu, Kai
    [J]. MEASUREMENT, 2021, 176
  • [9] Exploring Test-Retest Reliability and Longitudinal Stability of Digital Biomarkers for Parkinson Disease in the m-Power Data Set: Cohort Study
    Far, Mehran Sahandi
    Eickhoff, Simon B.
    Goni, Maria
    Dukart, Juergen
    [J]. JOURNAL OF MEDICAL INTERNET RESEARCH, 2021, 23 (09)
  • [10] Spatiotemporal patterns of population in mainland China, 1990 to 2010
    Gaughan, Andrea E.
    Stevens, Forrest R.
    Huang, Zhuojie
    Nieves, Jeremiah J.
    Sorichetta, Alessandro
    Lai, Shengjie
    Ye, Xinyue
    Linard, Catherine
    Hornby, Graeme M.
    Hay, Simon I.
    Yu, Hongjie
    Tatem, Andrew J.
    [J]. SCIENTIFIC DATA, 2016, 3