WiMi Hologram Cloud Inc. has developed a two-stage hybrid machine learning model using variational modal decomposition and support vector regression to efficiently capture dynamic…
Browsing: Feature Selection
Researchers have developed a machine learning-based heart disease prediction model that uses various combinations of information and recognized categorization methods. The model shows promising…
This article discusses the use of advanced machine learning and deep learning techniques in diagnosing and predicting heart disease. By incorporating feature selection and…
This article discusses the challenges of processing and analyzing large amounts of data in practical applications, particularly in data fusion and feature selection. It…
This article discusses the importance of feature selection in machine learning, particularly in the face of Big Data. It highlights the benefits of feature…
This article discusses the use of machine learning techniques to predict non-alcoholic steatohepatitis (NASH) based on clinical and blood data. The study found that…
This article discusses the potential of machine learning and big data in predicting type 2 diabetes. The study aims to identify the most significant…
This study examines the role of feature selection methods in optimizing machine learning algorithms for predicting heart disease. The Cleveland Heart disease dataset with…
This observational study assessed the utility of radiomics in differentiating between benign and malignant lung nodules detected on computed tomography (CT) scans. Employing random…
This article presents a review of various deep and machine learning techniques used to identify various complications in diabetic retinopathy (DR). The authors propose…