首页|New Machine Learning Study Results from First Affiliated Hospital of Zhengzhou U niversity Described [Machine learning approaches for predicti ng frailty base on multimorbidities in US adults using NHANES data (1999-2018)]
New Machine Learning Study Results from First Affiliated Hospital of Zhengzhou U niversity Described [Machine learning approaches for predicti ng frailty base on multimorbidities in US adults using NHANES data (1999-2018)]
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By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News-New research on artificial intelligence is the su bject of a new report. According to news originating from First Affiliated Hospi tal of Zhengzhou University by NewsRx correspondents, research stated, "The glob al increase in an aging population has led to more common age-related health cha llenges, particularly multimorbidity and frailty, but there is a significant gap . This cross-sectional study utilized data from the National Health and Nutritio n Examination Survey (1999-2018)." Financial supporters for this research include The First Affiliated Hospital of Zhengzhou University. Our news correspondents obtained a quote from the research from First Affiliated Hospital of Zhengzhou University: "The association between age and frailty was assessed using a restricted cubic spline (RCS) model, while weighted adjusted mu ltivariable logistic regression evaluated the effect of diseases to frailty. And in machine learning process, feature selection for the frailty prediction model involved three algorithms. The model's performance was optimized using nested c ross-validation and tested with various algorithms including decision tree, Logi stic Regression, k-Nearest Neighbor, Random Forest, Recursive Partitioning and R egression Trees, and eXtreme Gradient Boosting (XGBoost). We used areas under th e receiver operating characteristic curve (AUC) and area under the precision-rec all curve (AU-PRC) to evaluate six algorithms, select the optimal model, and tes t the discrimination and consistency of the optimal model. The study included 46 ,187 participants, with 6,009 cases of frailty. RCS analysis showed a non-linear association between age and frailty, with a turning point at 49 years. Key impa cting variables identified are Anemia, Arthritis, Diabetes Mellitus, Coronary He art Disease, and Hypertension. In the machine learning process, we selected the optimal data set by feature selection, including 13 variables. Through nested cr oss-validation, a total of 31,900 models were built using 6 algorithms. And the XGBoost model showed the highest performance (AUC = 0.8828 and AU-PRC = 0.624), and clear proficiency in both discrimination and calibration."
First Affiliated Hospital of Zhengzhou U niversityAlgorithmsCyborgsEmerging TechnologiesMachine Learning
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