In-Vitro Classification of Saliva Samples of COPD Patients and Healthy Controls Using Machine Learning Tools

Abstract

Chronic Obstructive Pulmonary Disease (COPD) is a life-threatening lung disease and a major cause of morbidity and mortality worldwide. Although a curative therapy has yet to be found, permanent monitoring of biomarkers that reflect the disease progression plays a pivotal role for the effective management of COPD. The accurate examination of respiratory tract fluids like saliva is a promising approach for staging disease and predicting its upcoming exacerbations in a Point-of-Care (PoC) environment. However, the concurrent consideration of patients' demographic and medical parameters is necessary for achieving accurate outcomes. Therefore, Machine Learning (ML) tools can play an important role for analyzing patient data and providing comprehensive results for the recognition of COPD in a PoC setting. As a result, the objective of this research work was to implement ML tools on data acquired from characterizing saliva samples of COPD patients and healthy controls as well as their demographic information for PoC recognition of the disease. For this purpose, a permittivity biosensor was used to characterize dielectric properties of saliva samples and, subsequently, ML tools were applied on the acquired data for classification. The XGBoost gradient boosting algorithm provided a high classification accuracy and sensitivity of 91.25% and 100%, respectively, making it a promising model for COPD evaluation. Integration of this model on a neuromorphic chip, in the future, will enable the real-time assessment of COPD in PoC, with low cost, low energy consumption, and high patient privacy. In addition, constant monitoring of COPD in a near-patient setup will enable the better management of the disease exacerbations.

A Hybrid Model to Classify Patients with Chronic Obstructive Respiratory Diseases

“…Several monitoring systems have been proposed in the context of CORD management over the last years, but these show evident limitations that should be discussed. A great number of existing proposals already combine different machine learning techniques in order to monitor the health condition of the patient [33][34][35][36][37][38] and provide personalized interactions. In this sense, we have seen systems using techniques such as fuzzy classifiers, artificial neural networks [34,[37][38][39], reinforcement learning [40,41], among others.…”

“…A great number of existing proposals already combine different machine learning techniques in order to monitor the health condition of the patient [33][34][35][36][37][38] and provide personalized interactions. In this sense, we have seen systems using techniques such as fuzzy classifiers, artificial neural networks [34,[37][38][39], reinforcement learning [40,41], among others. However, in the context of CORD management, this monitorization is mainly performed with the goal to analyse patient data and detect respiratory diseases or respiratory complications such as exacerbations [36][37][38] rather than understanding the profile (and associated behaviours) of the patient and anticipating further complications.…”

“…In this sense, we have seen systems using techniques such as fuzzy classifiers, artificial neural networks [34,[37][38][39], reinforcement learning [40,41], among others. However, in the context of CORD management, this monitorization is mainly performed with the goal to analyse patient data and detect respiratory diseases or respiratory complications such as exacerbations [36][37][38] rather than understanding the profile (and associated behaviours) of the patient and anticipating further complications. This means that in some way most existing systems are less proactive and more reactive to the current health condition of the patient.…”

“…This means that in some way most existing systems are less proactive and more reactive to the current health condition of the patient. Another issue which may compromise the usability of this kind of systems is that it often requires the use of multiple devices such as a smartphone solution combined with a digital spirometer to analyse respiratory function [36][37][38], which bring additional costs to the average user. With these points in mind, PHE project was designed to provide a solution which relies only upon the use of a smartphone and its embedded sensors to correctly monitor and capture patient data through the application of innovative monitoring algorithms.…”

The Potential of Machine Learning Algorithms in Discriminating Chronic Obstructive Pulmonary Disease and Healthy Saliva Samples

“…The smoking status, weight, age, gender, cytokine level, and pathogen load are some important issues in this regard (9)(10)(11).…”