Early detection for elderly people with musculoskeletal aging related diseases based on artificial intelligence model

Late-diagnosis is one of the main bottlenecks in musculoskeletal aging-related diseases prevention, and it is urgent to build early detection model. Twenty-two features were included to build early detection models based on binary and multiple classification respectively by XGBoost. In testing, the accuracy rate (63.74%~92.40%) and AUC (0.74 ~ 0.96) of binary-classification models were higher than the accuracy rate (61.40% ~85.96%) and AUC (0.63 ~ 0.86) of multiple-classification models. The optimal binary-classification model had an accuracy rate of 87.13% and an AUC of 0.92 in testing, including cooking, drinking milk, electronic devices use time, dental implant, dental decay, professional oral cleaning, falls in the past year, life satisfaction, the degree of pain or discomfort, indoor air improvement, drinking, body mass index, time spent indoors, grip grouping, SARC-F grouping, calf girth grouping and bone density examination. In elderly, musculoskeletal aging-related diseases can be early detected by model based on epidemiological factors.