Lidom: A Disease Risk Prediction Model Based on LightGBM Applied to Nursing Homes

Abstract

With the innovation of technologies such as sensors and artificial intelligence, some nursing homes use wearable devices to monitor the movement and physiological indicators of the elderly and provide prompts for any health risks. Nevertheless, this kind of risk warning is a decision based on a particular physiological indicator. Therefore, such decisions cannot effectively predict health risks. To achieve this goal, we propose a model Lidom (A LightGBM-based Disease Prediction Model) based on the combination of the LightGBM algorithm, InterpretML framework, and sequence confrontation network (SeqGAN). The Lidom model first solves the problem of uneven samples based on the sequence confrontation network (SeqGAN), then trains the model based on the LightGBM algorithm, uses the InterpretML framework for analysis, and finally obtains the best model. This paper uses the public dataset MIMIC-III, subject data, and the early diabetes risk prediction dataset in UCI as sample data. The experimental results show that the Lidom model has an accuracy rate of 93.46% for disease risk prediction and an accuracy rate of 99.8% for early diabetes risk prediction. The results show that the Lidom model can provide adequate support for the prediction of the health risks of the elderly.