首页|First Hospital of Shanxi Medical University Reports Findings in Machine Learning (Machine learning methods for adult OSAHS risk prediction)
First Hospital of Shanxi Medical University Reports Findings in Machine Learning (Machine learning methods for adult OSAHS risk prediction)
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By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News-New research on Machine Learning is th e subject of a report. According to news reporting originating in Taiyuan, Peopl e's Republic of China, by NewsRx journalists, research stated, "Obstructive slee p apnea hypopnea syndrome (OSAHS) is a common disease that can cause multiple or gan damage in the whole body. Our aim was to use machine learning (ML) to build an independent polysomnography (PSG) model to analyze risk factors and predict O SAHS." The news reporters obtained a quote from the research from the First Hospital of Shanxi Medical University, "Clinical data of 2064 snoring patients who underwen t physical examination in the Health Management Center of the First Affiliated H ospital of Shanxi Medical University from July 2018 to July 2023 were retrospect ively collected, involving 24 characteristic variables. Then they were randomly divided into training group and verification group according to the ratio of 7:3 . By analyzing the importance of these features, it was concluded that LDL-C, Cr , common carotid artery plaque, A1c and BMI made major contributions to OSAHS. M oreover, five kinds of machine learning algorithm models such as logistic regres sion, support vector machine, Boosting, Random Forest and MLP were further estab lished, and cross validation was used to adjust the model hyperparameters to det ermine the final prediction model. We compared the accuracy, Precision, Recall r ate, F1-score and AUC indexes of the model, and finally obtained that MLP was th e optimal model with an accuracy of 85.80%, Precision of 0.89, Reca ll of 0.75, F1-score of 0.82, and AUC of 0.938. We established the risk predicti on model of OSAHS using ML method, and proved that the MLP model performed best among the five ML models."
TaiyuanPeople's Republic of ChinaAsiaCyborgsEmerging TechnologiesMachine LearningRisk and Prevention
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