Estimating Time to Progression of Chronic Obstructive Pulmonary Disease with Tolerance

Abstract

Chronic Obstructive Pulmonary Disease (COPD) is a progressive respiratory disease that affects millions of people worldwide. Predicting the time to progression of COPD is critical for optimizing patient care and treatment planning. However, COPD progression is complex and multifactorial, and estimating time to progression accurately remains challenging. In this study, we propose a novel approach for estimating time to progression of COPD using a Random Forest model. Random Forest is an ensemble machine learning technique that combines multiple decision trees to make predictions. We incorporated the concept of tolerance into our model, which allows for variability in COPD progression rates among individuals. We utilized a dataset of COPD patients, including clinical and demographic variables, collected over a multi-year period. The dataset was divided into training and testing sets for model development and evaluation, respectively. The Random Forest model was trained on the training set using features such as age, gender, smoking history, lung function parameters, and comorbidities. The Random Forest model with tolerance outperformed traditional models in estimating time to progression of COPD. The tolerance concept allowed the model to account for inter-individual variability in COPD progression rates, which is a critical aspect of COPD prognosis. The model achieved high accuracy and robustness in predicting time to progression of COPD in the testing set, indicating its potential clinical utility. A novel approach for estimating time to progression of COPD using a Random Forest model with tolerance. This approach has the potential to improve the accuracy of COPD prognosis, leading to better patient care and treatment planning. Further validation studies are warranted to validate the clinical utility of our proposed model in diverse COPD populations.This project describes the pulmonary chronic disease detection and classification is done with different observing similar disease conditions or functional status from the upper to the lower boundaries of a specified time interval Different types of radiological report is analyzed for different radiologic reports disease detection and classification is done with different radiological reports The proposed model is compared with different machine learning models compared with different stages of COPD Chronic Obstructive Pulmonary Disease