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

Abstract

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