A predictive model of cognitive impairment in Parkinson's disease based on multivariate logistic regression - BA Mengru; YIN Xiaohong; LI Shaoyuan (智能科学与技术学报)
Parkinson's disease (PD) patients were often accompanied by cognitive impairment, which seriously affected the quality of life, so the over-prediction of cognitive impairment in Parkinson's disease was crucial for clinical diagnosis and intervention. However, Parkinson's disease was affected by the coupling of multivariate factors, such as age, gender, and disease duration, which made the overprediction of cognitive impairment a serious challenge. Aiming at the multivariate coupling characteristics of cognitive impairment in Parkinson's disease, in this study, multivariate logistic regression was used to construct a novel column-linear graphical model aiming at over-predicting the risk of cognitive impairment (CI) in Parkinson's disease patients. First, the least absolute shrinkage and selection operator (LASSO) algorithm was applied to analyze the risk factors that may affect the cognitive ability of patients, and the clinical variables with high correlation were screened out. Second, multivariate logistic regression was used to analyze the correlation between variables and construct a visualized novel column-line diagram model to achieve the risk over prediction of cognitive impairment in Parkinson's disease. Finally, the results of model performance evaluation show that the novel cognitive impairment prediction model proposed in this paper has good accuracy, consistency and clinical practicability, which can significantly improve the diagnostic efficiency of clinicians; in addition, the model also realizes the visual comparison and analysis of the number and distribution of patients with different values of the same predictor, which can assist clinicians in formulating personalized healthcare management and consulting programs according to the individual risk of each patient, and help to start the intervention and treatment of the patients at an early stage, and it has a certain clinical diagnostic value.