查看更多>>摘要：By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News-New research on Machine Learning is the subject o f a report. According to news originating from Chengdu, People's Republic of Chi na, by NewsRx correspondents, research stated, "Interpretable machine learning m odels are instrumental in disease diagnosis and clinical decision-making, sheddi ng light on relevant features. Notably, Boruta, SHAP (SHapley Additive exPlanati ons), and BorutaShap were employed for feature selection, each contributing to t he identification of crucial features." Our news journalists obtained a quote from the research from the University of E lectronic Science and Technology of China, "These selected features were then ut ilized to train six machine learning algorithms, including LR, SVM, ETC, AdaBoos t, RF, and LR, using diverse medical datasets obtained from public sources after rigorous preprocessing. The performance of each feature selection technique was evaluated across multiple ML models, assessing accuracy, precision, recall, and F1-score metrics. Among these, SHAP showcased superior performance, achieving a verage accuracies of 80.17%, 85.13%, 90.00% , and 99.55% across diabetes, cardiovascular, statlog, and thyroid disease datasets, respectively. Notably, the LGBM emerged as the most effective algorithm, boasting an average accuracy of 91.00% for most diseas e states. Moreover, SHAP enhanced the interpretability of the models, providing valuable insights into the underlying mechanisms driving disease diagnosis. This comprehensive study contributes significant insights into feature selection tec hniques and machine learning algorithms for disease diagnosis, benefiting resear chers and practitioners in the medical field."
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