首页|Data on Machine Learning Reported by Xiefei Hu and Colleagues (An exploration on the machine-learning-based stroke prediction model)
Data on Machine Learning Reported by Xiefei Hu and Colleagues (An exploration on the machine-learning-based stroke prediction model)
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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 from Chongqing, P eople's Republic of China, by NewsRx correspondents, research stated, "With the rapid development of artificial intelligence technology, machine learning algori thms have been widely applied at various stages of stroke diagnosis, treatment, and prognosis, demonstrating significant potential. A correlation between stroke and cytokine levels in the human body has recently been reported." Our news editors obtained a quote from the research, "Our study aimed to establi sh machine-learning models based on cytokine features to enhance the decision-ma king capabilities of clinical physicians. This study recruited 2346 stroke patie nts and 2128 healthy control subjects from Chongqing University Central Hospital . A predictive model was established through clinical experiments and collection of clinical laboratory tests and demographic variables at admission. Three clas sification algorithms, namely Random Forest, Gradient Boosting, and Support Vect or Machine, were employed. The models were evaluated using methods such as ROC c urves, AUC values, and calibration curves. Through univariate feature selection, we selected 14 features and constructed three machine-learning models: Support Vector Machine (SVM), Random Forest (RF), and Gradient Boosting Machine (GBM). O ur results indicated that in the training set, the RF model outperformed the GBM and SVM models in terms of both the AUC value and sensitivity. We ranked the fe atures using the RF algorithm, and the results showed that IL-6, IL-5, IL-10, an d IL-2 had high importance scores and ranked at the top. In the test set, the st roke model demonstrated a good generalization ability, as evidenced by the ROC c urve, confusion matrix, and calibration curve, confirming its reliability as a p redictive model for stroke. We focused on utilizing cytokines as features to est ablish stroke prediction models."
ChongqingPeople's Republic of ChinaA siaCyborgsEmerging TechnologiesMachine LearningSupport Vector MachinesVector Machines
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