INTERNATIONAL JOURNAL OF LATEST TECHNOLOGY IN ENGINEERING,

MANAGEMENT & APPLIED SCIENCE (IJLTEMAS)

ISSN 2278-2540 | DOI: 10.51583/IJLTEMAS | Volume XIII, Issue VII, July 2024

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Anomaly Based Detection of Chronic Obstructive Pulmonary Disease

Using Machine Learning

Ubani Kingsley Chukwuemeka, Nweke Benedine Chinelo, Ezeh Raymond Nonso

Department of Computer Science, Federal Polytechnic Oko

DOI : https://doi.org/10.51583/IJLTEMAS.2024.130701

the system's generalizability across different populations, and to explore opportunities for real-world implementation. The study's

findings can contribute to the development of more effective and efficient COPD management strategies, potentially leading to

improved patient outcomes and reduced healthcare costs.

Keywords: Anomaly detection, Chronic obstructive pulmonary disease (COPD), Machine learning, Respiratory patterns.

I. Introduction

Chronic Obstructive Pulmonary Disease (COPD) is a progressive respiratory disease that affects millions of people globally and is

responsible for significant morbidity and mortality (Sethi et al., 2019). Early detection and management of COPD is critical for

improving patient outcomes and reducing the burden on healthcare systems. Conventional methods for COPD detection and

monitoring primarily rely on clinical symptoms, spirometry tests, and imaging techniques (Morgan et al., 2020). However, these

methods are often limited in their ability to detect anomalies or atypical patterns that deviate from normal behavior, leading to missed

promising approach for COPD detection. Recent advancements in machine learning algorithms, such as deep learning and

reinforcement learning, have shown great potential in the healthcare domain, including COPD diagnosis and management (Su et al.,

2021). In this study, we propose an anomaly-based machine learning framework for COPD detection that can identify deviations

from normal patterns associated with COPD, enabling earlier detection and intervention. The proposed framework will be based on

state-of-the-art machine learning algorithms, such as Autoencoder and Isolation Forest (Liu et al., 2020), which have demonstrated

strong performance in detecting anomalies in data.

We will collect a large dataset of COPD and non-COPD cases from electronic health records and clinical trials, and use this dataset

to train the machine learning models. The performance of the models will be evaluated using standard metrics for anomaly detection,

such as the Receiver Operating Characteristic (ROC) curve and the F1-score.

II. Materials and Methods

or signature, and plans to recognize authentic occurrences from the malignant ones. Without the downside of inconsistency detection,

it is solid for recognizing known assaults with low False Positive (FP) rate. However, this sort of IDSs can't recognize obscure

assaults or varieties of known ones.

System structure

The system structure for the proposed anomaly-based COPD detection system includes:

INTERNATIONAL JOURNAL OF LATEST TECHNOLOGY IN ENGINEERING,

MANAGEMENT & APPLIED SCIENCE (IJLTEMAS)

ISSN 2278-2540 | DOI: 10.51583/IJLTEMAS | Volume XIII, Issue VII, July 2024

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System Implementation

The system implementation will involve thorough testing and validation to ensure that the blockchain framework is functioning as

intended and provides adequate data protection for healthcare data. Here are the key steps in the testing and validation process:

INTERNATIONAL JOURNAL OF LATEST TECHNOLOGY IN ENGINEERING,

MANAGEMENT & APPLIED SCIENCE (IJLTEMAS)

ISSN 2278-2540 | DOI: 10.51583/IJLTEMAS | Volume XIII, Issue VII, July 2024

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and is design with several features to ensure its sustainability and effectiveness over the long term. The system use adaptive and

dynamic learning algorithms that can adapt to new data and patterns, such as deep learning to maintain accuracy even as new threats

and treatments emerge. The system also use a combination of supervised learning techniques to identify new patterns in the data and

to detect early signs of disease progression or complications. Finally, the system is design to integrate seamlessly with existing

Diagnosis Input Module

The Diagnosis Input Module of the proposed anomaly-based COPD detection system is responsible for collecting and preprocessing

data from various sources, such as electronic health records, clinical trials, and wearable devices. The module transforms the raw

data into a suitable format for analysis, including extracting relevant features, such as respiratory patterns and vital signs, and

performing noise reduction and standardization techniques. This module feeds the preprocessed data into the machine learning model

for training and testing, enabling the detection of COPD-related anomalies. This module provides the user with the interface to input

the necessary information needed for a proper diagnosis as shown in Figures 2 to 4. This user interface has been made easy to use as

queries are asked in plain English language so as not to limit the users. Using this interface, the user inputs all basic information, fills

desired information and symptoms.

Diagnosis Result Module

INTERNATIONAL JOURNAL OF LATEST TECHNOLOGY IN ENGINEERING,

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ISSN 2278-2540 | DOI: 10.51583/IJLTEMAS | Volume XIII, Issue VII, July 2024

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module is also responsible for storing the detection results in the blockchain ledger, ensuring a secure and transparent record of the

diagnosis.

Figure 4: screen shot of the symptoms result of the patient.

IV. Conclusion

In conclusion, the proposed anomaly-based COPD detection system using machine learning demonstrated high accuracy and

sensitivity in detecting COPD-related disease, with strong correlation with other measures of COPD severity. This framework offers

a promising solution for early detection and monitoring of COPD, with the potential to improve patient outcomes and reduce

healthcare costs. Further research is needed to validate the system in larger and more diverse populations, and to explore

opportunities for real-world implementation in clinical settings.