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