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  • A Hybrid Model to Classify Patients with Chronic Obstructive . . .
    A classification model with 90 4% prediction accuracy was generated combining agglomerative hierarchical clustering and decision tree classification techniques Furthermore, this model identified 5 clusters which describe characteristics of 5 different types of users according to 7 generated rules
  • Design and Analysis of a Meta Stratified Optimal Hybrid Model . . .
    The future scope on the Meta Stratified Optimal Hybrid Model (MSOHM) for COPD classification includes further validation of the model on larger and more diverse datasets to ensure its generalizability Additionally, the model could be extended to classify different stages of COPD severity or to differentiate COPD from other respiratory conditions
  • A robust chronic obstructive pulmonary disease classification . . .
    Statistical analysis is conducted to evaluate the classification performance of the KELM model using accuracy, precision, recall, F1-Score, specificity, MCC, ROC and AUC values
  • Automated Chronic Obstructive Pulmonary Disease (COPD . . .
    Thus, this research search aims to classify COPD to detect its subtypes using an advanced machine learning model Optimization models are incorporated in the proposed work to select the optimal features to improve classification accuracy compared to conventional methods
  • Deep learning on graphs for multi-omics classification of COPD
    Then we extended the existing ConvGNN methods to successfully integrate COPD-associated PPI, proteomics, and transcriptomics data and developed a prediction model for COPD classification This approach improves accuracy over several conventional classification methods and neural networks that do not incorporate network information
  • A Hybrid Feature Selection Model for Predicting Chronic . . .
    These features are further passed to the classification models to identify the performance of the features on various classification models According to the experimental data, the suggested hybrid feature selection model could predict COPD with a 95 18 percent accuracy and a Kappa Statistic of 0 9
  • Design and Analysis of a Meta Stratified Optimal Hybrid Model . . .
    The suggested robust MSOHM classification model offers significant advantages to medical professionals by enabling early prediction of COPD with high accuracy This eventually supports in preventing exacerbations of the COPD disease and ultimately saving lives Keywords Chronic Obstructive Pulmonary Disease (COPD) ·Hybrid machine





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