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

• URL: http://arxiv.org/abs/2404.15757v1
• Date: Wed, 24 Apr 2024 09:25:16 GMT
• Title: Exploring Machine Learning Algorithms for Infection Detection Using GC-IMS Data: A Preliminary Study
• Authors: Christos Sardianos, Chrysostomos Symvoulidis, Matthias Schlögl, Iraklis Varlamis, Georgios Th. Papadopoulos,
• Abstract summary: Our research aims to tackle the ongoing issue of precise infection identification. By utilizing Gas Chromatography-Ion Mobility Spectrometry (GC-IMS) data and incorporating machine learning algorithms into one platform, our research aims to tackle the issue.
• Score: 2.4961885884659987
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

Related papers

• TopOC: Topological Deep Learning for Ovarian and Breast Cancer Diagnosis [3.262230127283452]
  Topological data analysis offers a unique approach by extracting essential information through the evaluation of topological patterns across different color channels. We show that the inclusion of topological features significantly improves the differentiation of tumor types in ovarian and breast cancers.
  arXiv  Detail & Related papers  (2024-10-13T12:24:13Z)
• Addressing the Gaps in Early Dementia Detection: A Path Towards Enhanced Diagnostic Models through Machine Learning [0.0]
  The rapid global aging trend has led to an increase in dementia cases, including Alzheimer's disease. Traditional diagnostic techniques, such as cognitive tests, neuroimaging, and biomarker analysis, face significant limitations in sensitivity, accessibility, and cost. This study explores the potential of machine learning (ML) as a transformative approach to enhance early dementia detection.
  arXiv  Detail & Related papers  (2024-09-05T00:52:59Z)
• Self-Supervised Time-Series Anomaly Detection Using Learnable Data Augmentation [37.72735288760648]
  We propose a learnable data augmentation-based time-series anomaly detection (LATAD) technique that is trained in a self-supervised manner. LATAD extracts discriminative features from time-series data through contrastive learning. As per the results, LATAD exhibited comparable or improved performance to the state-of-the-art anomaly detection assessments.
  arXiv  Detail & Related papers  (2024-06-18T04:25:56Z)
• Comparative Study of Machine Learning Algorithms in Detecting Cardiovascular Diseases [0.0]
  The detection of cardiovascular diseases (CVD) using machine learning techniques represents a significant advancement in medical diagnostics. This study explores a comparative analysis of various machine learning algorithms, including Logistic Regression, Decision Tree, Random Forest, Gradient Boosting, Support Vector Machine (SVM), K-Nearest Neighbors (KNN), and XGBoost. The findings highlight the efficacy of ensemble methods and advanced algorithms in providing reliable predictions, thereby offering a comprehensive framework for CVD detection.
  arXiv  Detail & Related papers  (2024-05-27T11:29:54Z)
• Leveraging Deep Learning and Xception Architecture for High-Accuracy MRI Classification in Alzheimer Diagnosis [11.295734491885682]
  This study aims to classify MRI images using deep learning models to identify different stages of Alzheimer Disease. Our experimental results show that the deep learning framework based on the Xception model achieved a 99.6% accuracy rate in the multi-class MRI image classification task.
  arXiv  Detail & Related papers  (2024-03-24T16:11:27Z)
• The Significance of Machine Learning in Clinical Disease Diagnosis: A Review [0.0]
  This research investigates the capacity of machine learning algorithms to improve the transmission of heart rate data in time series healthcare metrics. The factors under consideration include the algorithm utilized, the types of diseases targeted, the data types employed, the applications, and the evaluation metrics.
  arXiv  Detail & Related papers  (2023-10-25T20:28:22Z)
• Dissecting Self-Supervised Learning Methods for Surgical Computer Vision [51.370873913181605]
  Self-Supervised Learning (SSL) methods have begun to gain traction in the general computer vision community. The effectiveness of SSL methods in more complex and impactful domains, such as medicine and surgery, remains limited and unexplored. We present an extensive analysis of the performance of these methods on the Cholec80 dataset for two fundamental and popular tasks in surgical context understanding, phase recognition and tool presence detection.
  arXiv  Detail & Related papers  (2022-07-01T14:17:11Z)
• Lung Cancer Lesion Detection in Histopathology Images Using Graph-Based Sparse PCA Network [93.22587316229954]
  We propose a graph-based sparse principal component analysis (GS-PCA) network, for automated detection of cancerous lesions on histological lung slides stained by hematoxylin and eosin (H&E) We evaluate the performance of the proposed algorithm on H&E slides obtained from an SVM K-rasG12D lung cancer mouse model using precision/recall rates, F-score, Tanimoto coefficient, and area under the curve (AUC) of the receiver operator characteristic (ROC)
  arXiv  Detail & Related papers  (2021-10-27T19:28:36Z)
• Inheritance-guided Hierarchical Assignment for Clinical Automatic Diagnosis [50.15205065710629]
  Clinical diagnosis, which aims to assign diagnosis codes for a patient based on the clinical note, plays an essential role in clinical decision-making. We propose a novel framework to combine the inheritance-guided hierarchical assignment and co-occurrence graph propagation for clinical automatic diagnosis.
  arXiv  Detail & Related papers  (2021-01-27T13:16:51Z)
• Domain Shift in Computer Vision models for MRI data analysis: An Overview [64.69150970967524]
  Machine learning and computer vision methods are showing good performance in medical imagery analysis. Yet only a few applications are now in clinical use. Poor transferability of themodels to data from different sources or acquisition domains is one of the reasons for that.
  arXiv  Detail & Related papers  (2020-10-14T16:34:21Z)
• Select-ProtoNet: Learning to Select for Few-Shot Disease Subtype Prediction [55.94378672172967]
  We focus on few-shot disease subtype prediction problem, identifying subgroups of similar patients. We introduce meta learning techniques to develop a new model, which can extract the common experience or knowledge from interrelated clinical tasks. Our new model is built upon a carefully designed meta-learner, called Prototypical Network, that is a simple yet effective meta learning machine for few-shot image classification.
  arXiv  Detail & Related papers  (2020-09-02T02:50:30Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.