ArticleSensors (Basel, Switzerland)2021
Artificial Breath Classification Using XGBoost Algorithm for Diabetes Detection.
Article in Sensors (Basel, Switzerland), 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers.
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The trial behind it
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Who cites it
19 citing papers in PubMed, 78 citations in OpenAlex.
- Construction and evaluation of a column chart model and a random forest model for predicting the prognosis of hydrodistention surgery in BPS/IC patients based on preoperative CD117, P2X3R, NGF, and TrkA levels.BMC medical informatics and decision making · 2023Trial
- Evolution of diabetes prediction using the fusion of ANOVA, ADASYN technique and XGBoost based on body composition data.Journal of diabetes and metabolic disorders · 2025Article
- The exhaled breath pattern as a potential method for biometrics identification.Scientific reports · 2025Article
- Noninvasive Total Cholesterol Level Measurement Using an E-Nose System and Machine Learning on Exhaled Breath Samples.ACS sensors · 2024Article
- Construction and SHAP interpretability analysis of a risk prediction model for feeding intolerance in preterm newborns based on machine learning.BMC medical informatics and decision making · 2024Article
- Enhanced Diabetes Detection and Blood Glucose Prediction Using TinyML-Integrated E-Nose and Breath Analysis: A Novel Approach Combining Synthetic and Real-World Data.Bioengineering (Basel, Switzerland) · 2024Article
- VirusPredictor: XGBoost-based software to predict virus-related sequences in human data.Bioinformatics (Oxford, England) · 2024Article
- Noninvasive Diabetes Detection through Human Breath Using TinyML-Powered E-Nose.Sensors (Basel, Switzerland) · 2024Article
- DiabeticSense: A Non-Invasive, Multi-Sensor, IoT-Based Pre-Diagnostic System for Diabetes Detection Using Breath.Journal of clinical medicine · 2023Article
- A Machine Learning Approach to Assess Patients with Deep Neck Infection Progression to Descending Mediastinitis: Preliminary Results.Diagnostics (Basel, Switzerland) · 2023Article
- VGG16 Feature Extractor with Extreme Gradient Boost Classifier for Pancreas Cancer Prediction.Journal of imaging · 2023Article
- Hybrid Models Based on Fusion Features of a CNN and Handcrafted Features for Accurate Histopathological Image Analysis for Diagnosing Malignant Lymphomas.Diagnostics (Basel, Switzerland) · 2023Article
- Measurement of Complex Permittivity for Rapid Detection of Liquid Concentration Using a Reusable Octagon-Shaped Resonator Sensor.Micromachines · 2023Article
- Research progress of electronic nose technology in exhaled breath disease analysis.Microsystems & nanoengineering · 2023Review
- The innovative model based on artificial intelligence algorithms to predict recurrence risk of patients with postoperative breast cancer.Frontiers in oncology · 2023Article
- Diagnosis by Volatile Organic Compounds in Exhaled Breath in Exhaled Breath from Patients with Gastric and Colorectal Cancers.International journal of molecular sciences · 2022Review
- Non-Contact Supervision of COVID-19 Breathing Behaviour With FMCW Radar and Stacked Ensemble Learning Model in Real-Time.IEEE transactions on biomedical circuits and systems · 2022Article
- Prediction and Screening Model for Products Based on Fusion Regression and XGBoost Classification.Computational intelligence and neuroscience · 2022Article
- Prediction Model of Bone Marrow Infiltration in Patients with Malignant Lymphoma Based on Logistic Regression and XGBoost Algorithm.Computational and mathematical methods in medicine · 2022Article
Corrections and comments
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Authors and funding
3 authors at 1 institution in 1 country.
Funding
Abstract
Exhaled breath analysis has become more and more popular as a supplementary tool for medical diagnosis. However, the number of variables that have to be taken into account forces researchers to develop novel algorithms for proper data interpretation. This paper presents a system for analyzing exhaled air with the use of various sensors. Breath simulations with acetone as a diabetes biomarker were performed using the proposed e-nose system. The XGBoost algorithm for diabetes detection based on artificial breath analysis is presented. The results have shown that the designed system based on the XGBoost algorithm is highly selective for acetone, even at low concentrations. Moreover, in comparison with other commonly used algorithms, it was shown that XGBoost exhibits the highest performance and recall.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.