Background: A disease screening service is a preventive healthcare service. Accurate and efficient disease examination based on continuous monitoring data obtained from diseased subjects is the basis of developing this service. The traditional disease screening method for a specific disease is designed according to the physical meaning of the collected data.
Methods: In this paper, a general disease detection statistical model based on monitoring data is proposed. By analyzing the distribution of data obtained from subjects who may have certain diseases, we used functional data analysis to establish a statistical model and obtain an efficient algorithm for parameter estimation.
Results: The proposed model is applied to a real example of an elderly fall risk screening service based on plantar pressure data collected from elderly individuals walking over obstacles. Reasonable intervals of the model parameters used to screen the elderly for fall risk are obtained from the training samples, which are used to estimate the fall risk of the elderly with the test samples.
Conclusions: The study shows that the foot plantar pressure measured in screening tests can be characterized by functional data analysis, and a linear mixed effect model can be used when time points are fixed. The restricted maximum likelihood technique is used for parameter estimation, and a nonlinear optimization algorithm is employed to iteratively determine the model parameters. This paper is to provide a method of detecting falls in the elderly based on statistical data rather than the physical meaning of collected data.