Exploring Machine Learning Algorithms for Infection Detection Using GC-IMS Data: A Preliminary Study

TL;DR

This study explores the integration of GC-IMS data with machine learning algorithms to improve infection detection accuracy, focusing on developing a robust diagnostic platform for clinical use.

Contribution

It introduces a novel combination of GC-IMS data and machine learning within a unified LIMS platform for infectious disease diagnosis.

Findings

Preliminary results show promising accuracy in differentiating infected from non-infected samples.

The approach demonstrates potential for early disease detection.

Ongoing work aims to improve model effectiveness and interpretability.

Abstract

The developing field of enhanced diagnostic techniques in the diagnosis of infectious diseases, constitutes a crucial domain in modern healthcare. By utilizing Gas Chromatography-Ion Mobility Spectrometry (GC-IMS) data and incorporating machine learning algorithms into one platform, our research aims to tackle the ongoing issue of precise infection identification. Inspired by these difficulties, our goals consist of creating a strong data analytics process, enhancing machine learning (ML) models, and performing thorough validation for clinical applications. Our research contributes to the emerging field of advanced diagnostic technologies by integrating Gas Chromatography-Ion Mobility Spectrometry (GC-IMS) data and machine learning algorithms within a unified Laboratory Information Management System (LIMS) platform. Preliminary trials demonstrate encouraging levels of accuracy when employing various ML algorithms to differentiate between infected and non-infected samples. Continuing endeavors are currently concentrated on enhancing the effectiveness of the model, investigating techniques to clarify its functioning, and incorporating many types of data to further support the early detection of diseases.