An Expert Diagnostic System to Automatically Identify Asthma and Chronic Obstructive Pulmonary Disease in Clinical Settings

Almir Badnjevic,Eddie Custovic,Lejla Gurbeta

doi:10.1038/s41598-018-30116-2

Abstract

Respiratory diseases such as asthma and chronic obstructive pulmonary disease (COPD), are affecting a huge percentage of the world’s population with mortality rates exceeding those of lung cancer and breast cancer combined. The major challenge is the number of patients who are incorrectly diagnosed. To address this, we developed an expert diagnostic system that can differentiate among patients with asthma, COPD or a normal lung function based on measurements of lung function and information about patient’s symptoms. To develop accurate classification algorithms, data from 3657 patients were used and then independently verified using data from 1650 patients collected over a period of two years. Our results demonstrate that the expert diagnostic system can correctly identify patients with asthma and COPD with sensitivity of 96.45% and specificity of 98.71%. Additionally, 98.71% of the patients with a normal lung function were correctly classified, which contributed to a 49.23% decrease in demand for conducting additional tests, therefore decreasing financial cost.

Highlights

The usage of computer-based methods in medical diagnoses are on the rise and are gradually improving the quality of medical services by utilizing larger datasets of symptoms and patient history, as well as diagnostic test results for diagnosis
Beginning in the 1990s and increasing in the 2000s, expert systems based on machine learning methods, such as artificial neural networks (ANNs) and fuzzy logic (FL) were used for the detection of different types of diseases, including respiratory diseases
Several studies focusing on the use of different types of ANN architectures for classification of respiratory diseases with high classification accuracies developed on various datasets have been undertaken[16,17,18,19,20]

Summary

Introduction

The usage of computer-based methods in medical diagnoses are on the rise and are gradually improving the quality of medical services by utilizing larger datasets of symptoms and patient history, as well as diagnostic test results for diagnosis. Beginning in the 1990s and increasing in the 2000s, expert systems based on machine learning methods, such as artificial neural networks (ANNs) and fuzzy logic (FL) were used for the detection of different types of diseases, including respiratory diseases. Earlier studies based their classification efforts predominantly on SPIR and/or IOS measured test results, i.e. static assessment of patients. We developed an EDS based on data from 3657 patients and utilized combined ANN and FL algorithms. Our results demonstrate that the developed EDS system is reliable and such an automated diagnosis tool would be beneficial for healthcare institutions, especially in primary care and remote healthcare institutions

Objectives

Methods

Results

Conclusion