Anatomization of Respiratory Diseases Using Machine Learning

Abstract

The respiratory system is frequently susceptible to various diseases, with chronic obstructive pulmonary disease (COPD) serving as a notable illustration. COPD is distinguished by an enduring lung ailment characterized by a gradual reduction in lung function over time. Precise prediction of respiratory diseases is of utmost importance, as a failure to do so can lead to fatal consequences. Timely diagnosis plays a pivotal role in reducing mortality rates. In this research, raw spirometry data undergoes a process of feature selection to identify relevant attributes. These selected characteristics are subsequently input into a classification system to distinguish between normal, obstructive, and restrictive cases. The study illustrates how the accuracy of classification algorithms, particularly in the field of machine learning, can be significantly improved through feature selection methods. The suggested study has significantly enhanced the accuracy of categorization using a variety of algorithms, such as Naïve Bayes, Support Vector Machine, Logistic Regression, and K-Nearest Neighbor. Among these algorithms, Logistic Regression emerges as the most accurate classifier in this specific context. This investigation underscores the critical importance of early detection and emphasizes the potential of machine learning techniques in enhancing the accuracy of diagnosing respiratory diseases, particularly COPD, which can have a profound impact on patient outcomes.