Multistage Ensemble Learning Model With Weighted Voting and Genetic Algorithm Optimization Strategy for Detecting Chronic Obstructive Pulmonary Disease

Chronic Obstructive Pulmonary Disease (COPD) is a life-threatening lung ailment and significant cause of morbidity fatality globally. The early detection COPD can provide timely proper medication reduce the mortality rate. To obtain treatment lessen death rate, this study proposes novel ensemble model: Multistage Ensemble model (MSEN) with an optimized weighted voting technique to detect help clinicians medication. In study, there are two pools classifiers created in which four placed each pool. These employed form models based on strategy. This combines those generated using MSEN model. genetic algorithm utilized optimize hyperparameters classifier weights grid search technique. employs K-Nearest Neighbors approach fill missing values, isolation forest remove outliers, LightGBM Recursive Feature Elimination for feature selection. An evaluation suggested conducted real-world Exasens dataset validate model's effectiveness exhibits that proposed obtains better performance provides superior than other machine learning existing benchmark techniques.