Pain prediction model based on machine learning and SHAP values for elders with dementia in Taiwan
Shwu-Feng Tsay1, Cheng-Yu Chang2, Sing Shueh Hung3
1Department of Nursing and Health Care, Ministry of Health and Welfare, No. 488, Sec. 6, Zhongxiao E. Rd., Nangang District, Taipei City 115, Taiwan; School of Nursing, National Taiwan University, No.1, Sec. 1, Jen Ai Rd, Taipei City 100, Taiwan; Department of Health Services Administration, College of Public Health, China Medical University, No. 100, Sec. 1, Jingmao Rd., Beitun Dist., Taichung City 406, Taiwan.
Introduction:
Pain conditions are common in elderly individuals, including those with dementia. However, symptoms associated with dementia may lead to poor recognition, assessment and management of pain. In this study, we incorporated the variables based on questionnaires into a machine learning algorithm to build a prediction model for the pain index of elderly individuals with dementia.
Materials And Methods:
In this study, 113 cases were collected through questionnaires and used to build prediction models for the patient's pain index. Three machine learning algorithms were incorporated for comparison in this study. To interpret the prediction model, SHapley additive explanations values were used to depict the ranking importance of variables and the relationship between features and pain index.
Results:
In the comparison of models, random forests with feature selection outperformed in terms of root mean square error and mean absolute error. A total of 11 features were selected based on embedded method. The results showed that the Karnofsky scale played a key role in predicting pain index for elderly individuals with dementia and was positively associated with pain index. Arthritis is the most important disease to predicting the pain index.
Conclusions:
Our findings provided the key insights to predict the pain index of elderly patients with dementia. In the future, it can be used to develop an application system or webpage, which can reduce the use of labour and improve the efficiency.
