Karthick, G S (2022) Chronic obstructive pulmonary disease prediction using Internet of things-spiro system and fuzzy-based quantum neural network classifier. Theoretical Computer Science. pp. 1-22. ISSN 0304-3975

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Abstract

Chronic Obstructive Pulmonary Disease (COPD) is a multifarious progressive disease that
increases the mortality and morbidity ratio as well as becoming a life-threatening issue of
an individual. Accurate and cost-effective diagnosis of diseases plays a primary role in the
medical domain and a wide range of research has been carried out on disease prediction
using sensory approaches along with the assistance of machine learning techniques. The
traditional disease diagnosis procedures are invasive, costlier and the decision support
systems were unreliable most of the time. The human exhaled breath discharged from
the body is composed of various Volatile Organic Compounds (VOCs) which can be
influenced by metabolic and disease activities. Hence, the analysis of VOCs in exhaled
breath has an incredible potentiality for COPD diagnosis and can rapidly decrease the
mortality rate. In this research, IoT-Spiro System is designed and an intelligent machine
learning forecasting framework (IMLFF) has been proposed. IoT-Spiro System perceives the
various VOCs patterns available in exhaled breath and that real-time parameter has been
analyzed using IMLFF. The proposed framework incorporates a hybrid Genetic Big Bang-Big
Crunch (GBB-BC) algorithm for selecting the optimal features from the real-time dataset
and a Fuzzy-based Quantum Neural Network (F-QNN) classifier for diagnosing COPD. The
experimental results illustrate that IMLFF outperforms when compared to recent existing
approaches concerning various statistical parameters and performance metrics. From the
result analysis, it has been determined that IoT-Spiro System and IMLFF framework can
serve as an efficient assisting model to the medical practitioner for diagnosing COPD.

Item Type: Article
Uncontrolled Keywords: Keywords volatile organic compounds Exhaled breath IoT Spiro-system COPD diagnosis Feature selection Machine learning classifiers
Depositing User: Mr Team Mosys
Date Deposited: 20 Oct 2022 05:45
Last Modified: 20 Oct 2022 05:45
URI: http://ir.psgcas.ac.in/id/eprint/1598

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