A Review of Machine Learning-Assisted Gas Sensor Arrays in Medical Diagnosis
Abstract
1. Introduction
2. Common Types of Gas Sensors
2.1. Electrochemical Sensor
2.2. Optical Sensor
2.3. Semiconductor Sensor
3. Machine Learning for Gas Sensors
3.1. Supervised Learning
3.1.1. Linear Regression
3.1.2. Support Vector Machine
3.1.3. Artificial Neural Network
3.1.4. Random Forest
3.1.5. Linear Discriminant Analysis
3.2. Unsupervised Learning
4. Application in Disease Diagnosis
4.1. Respiratory Disorder
4.1.1. Lung Cancer and Chronic Obstructive Pulmonary Disease
4.1.2. Asthma
4.2. Metabolism and Nutrition Disorders
4.3. Hepatobiliary Disorders
4.4. Gastrointestinal Disorders
4.5. Nervous System Disorders
4.6. Renal and Urinary Disorders
4.6.1. Nephropathy
4.6.2. Bladder Cancer
4.7. Reproductive System Disorders
4.7.1. Breast Cancer
4.7.2. Prostate Cancer
5. Challenges and Development Directions
Author Contributions
Funding
Informed Consent Statement
Conflicts of Interest
References
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Disease | Sensor Array | Sensor Type | Material | Algorithm | Gas Markers | Parameter | References | |||||
---|---|---|---|---|---|---|---|---|---|---|---|---|
Lung cancer and COPD | 8 independent sensors form a heterogeneous sensor array | Electrochemical | SnO2-based sensitive film, Pt filament carrier + Pd/Al2O3 catalyst, precious metal electrode (Pt/Au) + liquid electrolyte | KPCA + XGBoost/AdaBoost/RF | Lung cancer | Formaldehyde | 0–1000 ppm | Accuracy: 84.75% Sensitivity: 81.36% Specificity: 88.14% | [123] | |||
Benzene series substances | 0–100 ppm | |||||||||||
Alkanes | 1–10,000 ppm | |||||||||||
CO | 1–100 ppm | |||||||||||
Ethanol | 30–5000 ppm | |||||||||||
Methane | 1–100 ppm | |||||||||||
COPD | Isobutane | 300–10,000 ppm | ||||||||||
Ethanol | 30–5000 ppm | |||||||||||
CO | 1–100 ppm | |||||||||||
Ammonia | 30–300 ppm | |||||||||||
32-channel sensor array | Resistive | Carbon nanotubes, polymer substrates | LDA + SVM | Ethanol | ppb level | AUC = 0.90, 0.91 Sensitivity: 75.0%, 83.3% Specificity: 96.6%, 85.4% | [124] | |||||
Isopropyl alcohol | ||||||||||||
Lipid peroxide-related VOCs | ||||||||||||
12-channel sensor array | Mechanical | Silica/titanium dioxide-based hybrid nanoparticles, commercial polymers | RF classifier | VOCs | Accuracy: 80.9% | [125] | ||||||
Asthma | Single-channel heterojunction | Resistive | N-type γ-Bi2MoO6 microspheres | PCA | H2S | 5 ppb–100 ppm | The response value of 5 ppb H2S = 1.5 100 ppb H2S is = 4.9 | [126] | ||||
NO | >50 ppb | |||||||||||
Virtual multi-wavelength array | Optical | Quartz, UV-grade fused quartz, metal | PCA + Match/No-match | NO | >40 ppb | Sensitivity 88% AUC-ROC 0.948 Specificity: 89% | [127] | |||||
Diabetes | Cross-response model array | Resistive | MOS | Baseline correction + KPCA + AdaBoost + MVRVM + GS/PSO | Acetone | 0.1–19.8 ppm | [128] | |||||
A heterogeneous array composed of three independent sensors | Resistive | NiO, CuO, ZnO thin films | KNN, RF, DT, logistic regression, naive Bayes, LDA, ANN, SVM | Acetone | 100–2400 ppm | classification accuracy > 99% | [129] | |||||
Ethanol | 100–2400 ppm | |||||||||||
Interdigital electrode structure | Resistive | α-Fe2O3-MWCNT nanocomposites, Pt interdigital electrodes, Al2O3, Pt microheaters | CNN, Adam | Acetone | 0.5–50 ppm | Accuracy: 85% | [130] | |||||
8-channel sensor array | Resistive | Porous MXene framework | PCA, t-SNE, SVM | C4-C7 aldehydes, ketones, alcohols | 5–50 ppm | Accuracy: 91.7% Sensitivity: 88.9% Specificity: 96.8% | [131] | |||||
Scan parameters to obtain multi-dimensional data | Electrochemical | Ni-63 ionization source, parallel metal plate electrode | Two-dimensional wavelet transform + PCA + sparse logistic regression/RF/Gaussian process/SVM | VOCs | <1 year | Specificity: 100% Sensitivity: 92% | [132] | |||||
18 sensor arrays | Resistive | Composite metal oxide | One to four years | Specificity: 82% Sensitivity: 87% | ||||||||
Hepatic disease | 3 sensor modules | Resistive | SnO2, WO3, Pd | LDA | Alkanes, NO | ppb level | Accuracy: 95–100% | [133] | ||||
Three-electrode system | Electrochemical | Gold nanoparticles, glassy carbon electrodes | PCA, heat map analysis | MBMBP | 0.15–0.38 M | —— | [134] | |||||
5 commercial MQ sensors + 6 interdigitated sensors | Resistive | Composite metal oxides, WO3 nanowire-based | PCA, DFA, SVM | Methanol, dimethyl sulfide, ethanol, toluene | Accuracy: 98.33% AUC = 0.965 | [135] | ||||||
Five-electrode voltammetry array | Electrochemical | Au, Pd, Pt, glassy carbon (GC), Cu | Cu2+ and other electroactive substances | Accuracy: 97.50% AUC = 0.950 | ||||||||
Gastrointestinal disorders | 10 thick film MOS sensors | Resistive | Composite metal oxides | RF, neural networks | Aldehydes, acetone, 2-heptanone, p-xylene | AUC = 0.81 | [136] | |||||
Single channel ion separation system | Electrochemical | Metal electrode plates | FDA, wavelet transform | VOCs | Accuracy: >75% | [137] | ||||||
18 sensor arrays | Resistive | Composite metal oxides | PCA, DFA | |||||||||
Nervous system disorders | 32 chemical sensor array | Resistive | Carbon black-polymer composite materials | PCA, LDA | 1-butanol, 2-methylfuran | Sensitivity: 50–70% Accuracy: 68–77% | [138] | |||||
Cross-reactive array consisting of 40 sensors | Resistive | GNPs, SWCNTs | DFA | Benzaldehyde, phenylacetone | ppb to ppm level | Accuracy: 81% Sensitivity: 79% Specificity: 84% | [139] | |||||
Nephropathy | 23-unit metal–organic frameworks (MOFs) array | Resonant | Metal–organic framework | CLAC calculation, SVD, iterative numerical model | NH3 | 3.32 ± 2.19 ppm | Accuracy: 100% | [140] | ||||
The spiral porous structure printed by DLP 3D printing | Optical | NAGA, Gly, choline chloride, BCG | CNN | NH3 | 0.5–10 ppm | Accuracy: 96.5% | [141] | |||||
6 sensor array | Resistive | Composite metal oxide | PCA, HCA, SVM, PLS | Dichloromethane, 6-nitro-2-picoline, 4-amino-4H-1,2, 4-triazole, styrene, limonene | 5–20,000 ppm | Accuracy: 100% | [142] | |||||
Bladder cancer | 10-sensor array | Resistive | Polyaniline (substrate material) Fluorine-doped tin oxide (electrode) | PCA, SVM, Kmeans | Benzaldehyde, 2-pentanone, butylbenzene, etc. | 25–200 ppm | Accuracy: 96.67% Sensitivity: 100% Specificity: 83.33% | [143] | ||||
piperone | 7–50 ppm | |||||||||||
8 types of metal oxide gas sensor arrays | Resistive | Composite metal oxides | PCA, LDA, SVM, RF, KNN | VOCs | Accuracy: PCA + SVM: 97%; LDA + KNN: 97%; LDA + RF: 94% | [144] | ||||||
Breast cancer | 32-sensor array | Resistive | Carbon nanotubes | KNN, SVM, DT, neural network | VOCs | Accuracy: 91% Sensitivity: 86% Specificity: 97% | [145] | |||||
Prostate cancer | Various MEMS gas sensor arrays | Resistive | Composite metal oxides | PCA, SVM, KNN, RF, decision tree, naive Bayes | Ethanol | 0.3–100 ppm | Accuracy: 100% Sensitivity: 100% | [146] | ||||
Screen-printed electrode array | Electrochemical | Carbon-based electrodes, gold-based electrodes | Formaldehyde | 0.1–208 ppm | ||||||||
8 types of MXene-TMDC nanocomposite sensor arrays | Resistive | MXene-TMDC nanocomposites, transition metal dichalcogenides | PCA, hierarchical clustering analysis, CNN, LDA, logistic regression, RF, SVM | Glyoxal 4-Heptanone 2-Pentanone | 10–100 ppm | Accuracy: 90% Sensitivity: 98% Specificity: 97% | [147] |
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Yu, Y.; Cao, X.; Li, C.; Zhou, M.; Liu, T.; Liu, J.; Zhang, L. A Review of Machine Learning-Assisted Gas Sensor Arrays in Medical Diagnosis. Biosensors 2025, 15, 548. https://doi.org/10.3390/bios15080548
Yu Y, Cao X, Li C, Zhou M, Liu T, Liu J, Zhang L. A Review of Machine Learning-Assisted Gas Sensor Arrays in Medical Diagnosis. Biosensors. 2025; 15(8):548. https://doi.org/10.3390/bios15080548
Chicago/Turabian StyleYu, Yueting, Xin Cao, Chenxi Li, Mingyue Zhou, Tianyu Liu, Jiang Liu, and Lu Zhang. 2025. "A Review of Machine Learning-Assisted Gas Sensor Arrays in Medical Diagnosis" Biosensors 15, no. 8: 548. https://doi.org/10.3390/bios15080548
APA StyleYu, Y., Cao, X., Li, C., Zhou, M., Liu, T., Liu, J., & Zhang, L. (2025). A Review of Machine Learning-Assisted Gas Sensor Arrays in Medical Diagnosis. Biosensors, 15(8), 548. https://doi.org/10.3390/bios15080548