SYMPTOM-DRIVEN EARLY DETECTION OF EMPHYSEMA EMPLOYING DEEP LEARNING TECHNIQUES

Emphysema is a chronic and progressive pulmonary disorder characterized by irreversible destruction
of alveolar structures, leading to impaired respiratory function and reduced quality of life. Early
detection of emphysema is challenging because initial symptoms are often mild, nonspeciϐic, and easily
overlooked in routine clinical practice. Timely diagnosis, however, is crucial for initiating preventive
interventions, slowing disease progression, and reducing morbidity and healthcare costs. This study
presents a deep learning–based framework for the early detection of emphysema using symptom-level
data, aiming to support clinicians in identifying high-risk individuals before the disease reaches
advanced stages. The proposed approach utilizes a symptom-driven dataset comprising clinical
indicators such as chronic cough, shortness of breath, wheezing, chest tightness, fatigue, smoking
history, and demographic factors. After preprocessing steps including data normalization, missing-value
handling, and feature encoding, the dataset is used to train a deep neural network (DNN). The model
architecture consists of multiple hidden layers with nonlinear activation functions, enabling it to
capture complex relationships between symptoms and underlying disease patterns. To enhance
robustness and reduce over ϐitting, techniques such as dropout regularization and early stopping are
applied during training. Model performance is evaluated using standard metrics including accuracy,
precision, recall, F1-score, and area under the receiver operating characteristic curve (AUC).
Experimental results demonstrate that the deep learning model outperforms traditional machine
learning classiϐiers in predicting early-stage emphysema, achieving high sensitivity and speciϐicity. The
ϐindings indicate that symptom-based deep learning models can effectively identify subtle patterns that
may not be apparent through conventional diagnostic methods. This study highlights the potential of
deep learning algorithms as cost-effective, noninvasive decision-support tools for early emphysema
detection. Integrating such models into primary healthcare and telemedicine systems could facilitate
early screening, personalized risk assessment, and improved clinical outcomes.