Ligia Maria Nascimento Arantes, Andrey Oeiras Pedroso, Mayra Gonçalves Menegueti, Elucir Gir, Eliã Pinheiro Botelho, Ana Cristina de Oliveira E Silva, Renata Karina Reis
Factors Associated with Late Diagnosis of Human Immunodeficiency Virus/Acquired Immunodeficiency Syndrome (HIV/AIDS) in a University Hospital in Brazil: Challenges to Achieving the 2030 Target
Viruses
physical frailty important issue aging societies three models physical frailty assessment 5item fatigue resistance ambulation illness loss weight frail cardiovascular health study chs study osteoporotic fractures sof indices regularly used clinical research studies however previous studies investigated predictive ability machine learning ml physical frailty assessment aim use two ml algorithms random forest rf extreme gradient boosting xgboost predict three physical frailty assessment models questionnaires regarding demographic characteristics lifestyle habits living environment physical frailty assessment answered 445 participants aged 60 years rf xgboost algorithms used assess scores three physical frailty indices furthermore feature importance shapley additive explanations shap used determine important physical frailty factors xgboost algorithm obtained higher accuracy predicting three physical frailty indices areas curve obtained xgboost algorithm 5item frail chs sof indices 084 079 069 respectively feature importance shap xgboost algorithm revealed systolic blood pressure diastolic blood pressure age body mass index play important roles three physical frailty models xgboost algorithm accurate predictive rate rf across three physical frailty assessments thus ml useful tool early detection physical frailty