Quantum Machine Learning for Early Detection of Chronic Diseases
Article Sidebar
-
Chronic Diseases, Early Detection, Quantum Machine
Abstract
The background of this research focuses on t, Malaysiahe development of early detection methods for chronic diseases using Quantum Machine Learning (QML). Chronic diseases such as diabetes, hypertension, heart disease, and cancer are often detected too late, leading to preventable complications. This study aims to explore the potential of QML in improving the accuracy and speed of diagnosis by combining clinical data and medical images. The method used involves the application of quantum machine learning algorithms to analyze medical datasets that include numerical information and medical images such as CT scans and MRIs. The results show that QML can process data faster and more accurately than traditional machine learning methods. QML is also capable of detecting hidden patterns in data that cannot be found with conventional techniques. The conclusion of this study shows that Quantum Machine Learning offers an effective new approach for the early detection of chronic diseases. This technology can improve healthcare systems by providing faster and more accurate predictions, which can reduce mortality rates from chronic diseases. Further research is needed to expand QML applications and address current hardware limitations
Full text article
References
Abbas, A. H. (2021). Early detection and diagnosis of chronic obstructive pulmonary disease in asymptomatic male smokers and ex-smokers using spirometry. Revista Latinoamericana de Hipertension, 15(1), 44–50. https://doi.org/10.5281/zenodo.5106415
Abqari, U. (2022). Strategies to promote the use of online health applications for early detection and raising awareness of chronic diseases among members of the general public: A systematic literature review. International Journal of Medical Informatics, 162(Query date: 2024-11-30 00:57:25). https://doi.org/10.1016/j.ijmedinf.2022.104737
Batra, K. (2021). Quantum Machine Learning Algorithms for Drug Discovery Applications. Journal of Chemical Information and Modeling, 61(6), 2641–2647. https://doi.org/10.1021/acs.jcim.1c00166
Blance, A. (2021). Quantum machine learning for particle physics using a variational quantum classifier. Journal of High Energy Physics, 2021(2). https://doi.org/10.1007/JHEP02(2021)212
Chen, Y. (2022). Editorial: Toolkits for Prediction and Early Detection of Acute Exacerbations of Chronic Obstructive Pulmonary Disease. Frontiers in Medicine, 9(Query date: 2024-11-30 00:57:25). https://doi.org/10.3389/fmed.2022.899450
Cherneva, Z. (2021). The role of stress echocardiography in the early detection of diastolic dysfunction in non-severe chronic obstructive pulmonary disease patients. Arquivos Brasileiros de Cardiologia, 116(2), 259–265. https://doi.org/10.36660/abc.20190623
Chin, H. M. (2021). Machine learning aided carrier recovery in continuous-variable quantum key distribution. Npj Quantum Information, 7(1). https://doi.org/10.1038/s41534-021-00361-x
Cincio, L. (2021). Machine Learning of Noise-Resilient Quantum Circuits. PRX Quantum, 2(1). https://doi.org/10.1103/PRXQuantum.2.010324
Dey, D. (2022). Early Detection of Dwindling Cochlear Sensitivity in Patients with Chronic Kidney Disease. Otorhinolaryngology Clinics, 14(1), 17–21. https://doi.org/10.5005/jp-journals-10003-1423
Durgaprasad, B. K. (2022). Role of predictable biomarkers in early detection of cardiovascular events in Chronic Kidney Disease III and IV. Current Issues in Pharmacy and Medical Sciences, 35(3), 99–105. https://doi.org/10.2478/cipms-2022-0019
Dwyer, K. M. (2022). Impact of COVID-19 on the worsening crisis of chronic kidney disease: The imperative to fund early detection is now. Internal Medicine Journal, 52(4), 680–682. https://doi.org/10.1111/imj.15670
Gill, S. L. (2020). Qualitative Sampling Methods. Journal of Human Lactation, 36(4), 579–581. https://doi.org/10.1177/0890334420949218
Goto, T. (2021). Universal Approximation Property of Quantum Machine Learning Models in Quantum-Enhanced Feature Spaces. Physical Review Letters, 127(9). https://doi.org/10.1103/PhysRevLett.127.090506
Guan, W. (2021). Quantum machine learning in high energy physics. Machine Learning: Science and Technology, 2(1). https://doi.org/10.1088/2632-2153/abc17d
Han, J., Xu, K., Yan, Q., Sui, W., Zhang, H., Wang, S., Zhang, Z., Wei, Z., & Han, F. (2022). Qualitative and quantitative evaluation of Flos Puerariae by using chemical fingerprint in combination with chemometrics method. Journal of Pharmaceutical Analysis, 12(3), 489–499. https://doi.org/10.1016/j.jpha.2021.09.003
Houssein, E. H. (2022). Machine learning in the quantum realm: The state-of-the-art, challenges, and future vision. Expert Systems with Applications, 194(Query date: 2024-11-30 07:56:17). https://doi.org/10.1016/j.eswa.2022.116512
Huang, H. Y. (2021). Power of data in quantum machine learning. Nature Communications, 12(1). https://doi.org/10.1038/s41467-021-22539-9
Huang, H. Y. (2022). Provably efficient machine learning for quantum many-body problems. Science, 377(6613). https://doi.org/10.1126/science.abk3333
Ji, H., Qin, W., Yuan, Z., & Meng, F. (2021). Qualitative and quantitative recognition method of drug-producing chemicals based on SnO2 gas sensor with dynamic measurement and PCA weak separation. Sensors and Actuators B: Chemical, 348, 130698. https://doi.org/10.1016/j.snb.2021.130698
Jiulin, S., Quntao, Z., Xiaojin, G., & Jisheng, X. (2021). Quantitative Evaluation of Top Coal Caving Methods at the Working Face of Extra?Thick Coal Seams Based on the Random Medium Theory. Advances in Civil Engineering, 2021(1), 5528067. https://doi.org/10.1155/2021/5528067
Ju, C. W. (2021). Machine Learning Enables Highly Accurate Predictions of Photophysical Properties of Organic Fluorescent Materials: Emission Wavelengths and Quantum Yields. Journal of Chemical Information and Modeling, 61(3), 1053–1065. https://doi.org/10.1021/acs.jcim.0c01203
Kang, Y. (2021). Recent progress on discovery and properties prediction of energy materials: Simple machine learning meets complex quantum chemistry. Journal of Energy Chemistry, 54(Query date: 2024-11-30 07:56:17), 72–88. https://doi.org/10.1016/j.jechem.2020.05.044
Kudyshev, Z. A. (2021). Machine Learning for Integrated Quantum Photonics. ACS Photonics, 8(1), 34–46. https://doi.org/10.1021/acsphotonics.0c00960
Langer, M. F. (2022). Representations of molecules and materials for interpolation of quantum-mechanical simulations via machine learning. Npj Computational Materials, 8(1). https://doi.org/10.1038/s41524-022-00721-x
Mahendran, M., Lizotte, D., & Bauer, G. R. (2022). Quantitative methods for descriptive intersectional analysis with binary health outcomes. SSM - Population Health, 17, 101032. https://doi.org/10.1016/j.ssmph.2022.101032
Martín-Guerrero, J. D. (2022). Quantum Machine Learning: A tutorial. Neurocomputing, 470(Query date: 2024-11-30 07:56:17), 457–461. https://doi.org/10.1016/j.neucom.2021.02.102
Mazaheri, M. (2021). Serum Interleukin-6 and Interleukin-8 are Sensitive Markers for Early Detection of Pyelonephritis and Its Prevention to Progression to Chronic Kidney Disease. International Journal of Preventive Medicine, 12(1). https://doi.org/10.4103/ijpvm.IJPVM_50_19
Mishra, N. (2021). Quantum Machine Learning: A Review and Current Status. Advances in Intelligent Systems and Computing, 1175(Query date: 2024-11-30 07:56:17), 101–145. https://doi.org/10.1007/978-981-15-5619-7_8
Mizdrak, M. (2022). Emerging Biomarkers for Early Detection of Chronic Kidney Disease. Journal of Personalized Medicine, 12(4). https://doi.org/10.3390/jpm12040548
Mousavi, S. A. J. (2021). Diagnostic sensitivity of impulse oscillometry in early detection of patients exposed to risk factors chronic obstructive pulmonary diseases. Medical Journal of the Islamic Republic of Iran, 35(1), 1–4. https://doi.org/10.34171/mjiri.35.89
Mujal, P. (2021). Opportunities in Quantum Reservoir Computing and Extreme Learning Machines. Advanced Quantum Technologies, 4(8). https://doi.org/10.1002/qute.202100027
Nagib, S. N. (2021). Screening and early detection of chronic kidney disease at primary healthcare: Chronic Kidney Disease at Primary Care. Clinical and Experimental Hypertension, 43(5), 416–418. https://doi.org/10.1080/10641963.2021.1896726
Peters, E. (2021). Machine learning of high dimensional data on a noisy quantum processor. Npj Quantum Information, 7(1). https://doi.org/10.1038/s41534-021-00498-9
Rankine, C. D. (2021). Progress in the Theory of X-ray Spectroscopy: From Quantum Chemistry to Machine Learning and Ultrafast Dynamics. Journal of Physical Chemistry A, 125(20), 4276–4293. https://doi.org/10.1021/acs.jpca.0c11267
Romero, J. (2021). Variational Quantum Generators: Generative Adversarial Quantum Machine Learning for Continuous Distributions. Advanced Quantum Technologies, 4(1). https://doi.org/10.1002/qute.202000003
Sajjan, M. (2022). Quantum machine learning for chemistry and physics. Chemical Society Reviews, 51(15), 6475–6573. https://doi.org/10.1039/d2cs00203e
Saleh, B. H. O. (2022). Crucial biomarkers for early detection of chronic kidney disease; Neutrophil gelatinase-associated lipocalin (NGAL) and interleukin-6 (IL)-6. Eurasian Chemical Communications, 4(3), 272–278. https://doi.org/10.22034/ecc.2022.322991.1288
Thivel, D. (2022). Fine Detection of Human Motion During Activities of Daily Living as a Clinical Indicator for the Detection and Early Treatment of Chronic Diseases: The E-Mob Project. Journal of Medical Internet Research, 24(1). https://doi.org/10.2196/32362
Turner, J. (2021). Home Spirometry Telemonitoring for Early Detection of Bronchiolitis Obliterans Syndrome in Patients with Chronic Graft-versus-Host Disease: J. Turner et al. Transplantation and Cellular Therapy, 27(7), 616–616. https://doi.org/10.1016/j.jtct.2021.03.024
Wang, Y. (2021). TRACE: Early Detection of Chronic Kidney Disease Onset with Transformer-Enhanced Feature Embedding. Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 12921(Query date: 2024-11-30 00:57:25), 166–182. https://doi.org/10.1007/978-3-030-93663-1_13
Authors
Copyright (c) 2024 Sri Nur Rahmi, Bruna Costa, Rafaela Lima
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
