{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,27]],"date-time":"2026-08-27T05:18:22Z","timestamp":1787807902481,"version":"build-2784847793"},"reference-count":41,"publisher":"MDPI AG","issue":"17","license":[{"start":{"date-parts":[[2026,8,25]],"date-time":"2026-08-25T00:00:00Z","timestamp":1787616000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100005034","name":"Bina Nusantara University","doi-asserted-by":"crossref","award":["PI2026009"],"award-info":[{"award-number":["PI2026009"]}],"id":[{"id":"10.13039\/501100005034","id-type":"DOI","asserted-by":"crossref"}]},{"award":["PI2026009"],"award-info":[{"award-number":["PI2026009"]}],"id":[{"id":"https:\/\/ror.org\/03zmf4s77","id-type":"ROR","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Conventional diagnosis of chronic kidney disease (CKD) relies predominantly on invasive blood-based examinations, limiting the scalability of kidney health screening in resource-constrained environments. This study presents embedded engineering framework for non-invasive, breath-based CKD staging framework supported by machine learning and implemented on a low-cost embedded platform. To account for physiological sex differences in baseline creatinine production, estimated glomerular filtration rate (eGFR) values and breath ammonia concentrations were derived from two independent clinical cohorts using sex-specific MDRD equations (incorporating the standard male formula and the\u00a00.742\u00a0female correction factor, respectively) and creatinine\u2013BUN conversion models, with male- and female-parameterized algorithms developed in parallel. The resulting feature space was analyzed using four unsupervised clustering approaches to stratify subjects into five clinically meaningful kidney function stages. Stage-specific ammonia thresholds were implemented within an Arduino Nano-based prototype equipped with an MQ-137 gas sensor and OLED display, enabling real-time point-of-care classification. Dataset-level classification accuracy reached 82% for the male algorithm and 92% for the female algorithm. Hospital-based validation on 29 patients (22 male, 7 female) yielded a real-world testing accuracy of 90.5% (20\/22) for male patients and 71.4% (5\/7) for female patients, a discrepancy largely attributable to the small female sample size. Because the current evaluation lacks healthy control subjects and is constrained by sample size, these empirical results serve primarily to demonstrate hardware-software functional integration and real-world deployment feasibility rather than definitive clinical efficacy. Despite these preliminary, sample-limited clinical datasets, results suggest this approach holds promise as an accessible, non-invasive screening complement to conventional diagnostic pathways, particularly in low-resource healthcare settings.<\/jats:p>","DOI":"10.3390\/s26175371","type":"journal-article","created":{"date-parts":[[2026,8,25]],"date-time":"2026-08-25T12:57:17Z","timestamp":1787662637000},"page":"5371","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Development of a Low-Cost Portable Exhaled Breath Ammonia Detector for Supplementary Five-Stage CKD Classification Using Embedded Threshold Logic"],"prefix":"10.3390","volume":"26","author":[{"given":"Winda","family":"Astuti","sequence":"first","affiliation":[{"name":"Automotive and Robotics Program, Computer Engineering Department, BINUS ASO School of Engineering, Bina Nusantara University, Jakarta 11480, Indonesia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Juan Alexander","family":"Kwan","sequence":"additional","affiliation":[{"name":"Automotive and Robotics Program, Computer Engineering Department, BINUS ASO School of Engineering, Bina Nusantara University, Jakarta 11480, Indonesia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Elioenai","family":"Sitepu","sequence":"additional","affiliation":[{"name":"Industrial Engineering Department, Graduate Program, Master of Industrial Engineering, Bina Nusantara University, Jakarta 11480, Indonesia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0217-6301","authenticated-orcid":false,"given":"Syauqi Abdurrahman","family":"Abrori","sequence":"additional","affiliation":[{"name":"Automotive and Robotics Program, Computer Engineering Department, BINUS ASO School of Engineering, Bina Nusantara University, Jakarta 11480, Indonesia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4052-674X","authenticated-orcid":false,"given":"Feri","family":"Setiawan","sequence":"additional","affiliation":[{"name":"Computer Science Department, Bina Nusantara University, Jakarta 11480, Indonesia"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2026,8,25]]},"reference":[{"key":"ref_1","unstructured":"GBD 2023 Chronic Kidney Disease Collaborators (2025). Global, Regional, and National Burden of Chronic Kidney Disease in Adults, 1990\u20132023, and Its Attributable Risk Factors: A Systematic Analysis for the Global Burden of Disease Study 2023. Lancet, 406, 2461\u20132482."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"e382","DOI":"10.1016\/S2214-109X(23)00570-3","article-title":"An Update on the Global Disparities in Kidney Disease Burden and Care across World Countries and Regions","volume":"12","author":"Bello","year":"2024","journal-title":"Lancet Glob. Health"},{"key":"ref_3","first-page":"64","article-title":"Chronic Kidney Disease\u2014A Major Public Health Problem","volume":"12","author":"Groza","year":"2025","journal-title":"Mold. J. Health Sci."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"S117","DOI":"10.1016\/j.kint.2023.10.018","article-title":"KDIGO 2024 Clinical Practice Guideline for the Evaluation and Management of Chronic Kidney Disease","volume":"105","author":"Stevens","year":"2024","journal-title":"Kidney Int."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"461","DOI":"10.7326\/0003-4819-130-6-199903160-00002","article-title":"A More Accurate Method to Estimate Glomerular Filtration Rate from Serum Creatinine: A New Prediction Equation. Modification of Diet in Renal Disease Study Group","volume":"130","author":"Levey","year":"1999","journal-title":"Ann. Intern. Med."},{"key":"ref_6","unstructured":"Gounden, V., Bhatt, H., and Jialal, I. (2024). Renal Function Tests, National Library of Medicine."},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"George, C., and Kengne, A.P. (2024). Advances in the Diagnosis, Treatment, and Prognosis of Chronic Kidney Disease: A Reflection on Recent Developments. Appl. Sci., 14.","DOI":"10.3390\/app14135518"},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Alsbrooks, K., and Hoerauf, K. (2022). Prevalence, Causes, Impacts, and Management of Needle Phobia: An International Survey of a General Adult Population. PLoS ONE, 17.","DOI":"10.1371\/journal.pone.0276814"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"30","DOI":"10.1111\/jan.13818","article-title":"The Fear of Needles: A Systematic Review and Meta-Analysis","volume":"75","author":"McLenon","year":"2019","journal-title":"J. Adv. Nurs."},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Tricoli, A., and Neri, G. (2018). Miniaturized Bio-and Chemical-Sensors for Point-of-Care Monitoring of Chronic Kidney Diseases. Sensors, 18.","DOI":"10.3390\/s18040942"},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Usman, F., Dennis, J.O., Aljameel, A., Ali, M., Aldaghri, O., Ibnaouf, K., Zango, Z.U., Beygisangchin, M., Alsadig, A., and Meriaudeau, F. (2023). Detection of Kidney Complications Relevant Concentrations of Ammonia Gas Using Plasmonic Biosensors: A Review. Chemosensors, 11.","DOI":"10.3390\/chemosensors11020119"},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Chan, M.J., Cheng, C.Y., Tsai, T.H., Chien, C.H., and Tian, Y.C. (2020). Breath Ammonia Is a Useful Biomarker Predicting Kidney Function in Chronic Kidney Disease Patients. Biomedicines, 8.","DOI":"10.3390\/biomedicines8110468"},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"10413","DOI":"10.1038\/s41598-022-14152-7","article-title":"Utility of SIFT-MS to Evaluate Volatile Organic Compounds in Nephropathic Patients\u2019 Breath","volume":"12","author":"Romani","year":"2022","journal-title":"Sci. Rep."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"955","DOI":"10.1039\/D2SD00089J","article-title":"Ammonia Breath Analysis","volume":"1","author":"Lefferts","year":"2022","journal-title":"Sens. Diagn."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"16290","DOI":"10.1021\/acs.analchem.2c02760","article-title":"Design and Fabrication of a Gas Sensor Based on a Polypyrrole\/Silver Nanoparticle Film for the Detection of Ammonia in Exhaled Breath of COVID-19 Patients Suffering from Acute Kidney Injury","volume":"94","author":"Kamalabadi","year":"2022","journal-title":"Anal. Chem."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"2301884","DOI":"10.1002\/admt.202301884","article-title":"AI-Assisted Multimodal Breath Sensing System with Semiconductive Polymers for Accurate Monitoring of Ammonia Biomarkers","volume":"9","author":"Weisbecker","year":"2024","journal-title":"Adv. Mater. Technol."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"4286","DOI":"10.1021\/acssensors.4c01525","article-title":"Ppb-Level Ammonia Sensor for Exhaled Breath Diagnosis Based on UV-DOAS Combined with Spectral Reconstruction Fitting Neural Network","volume":"9","author":"Zhu","year":"2024","journal-title":"ACS Sens."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"17","DOI":"10.1016\/j.future.2020.04.036","article-title":"Detection and Diagnosis of Chronic Kidney Disease Using Deep Learning-Based Heterogeneous Modified Artificial Neural Network","volume":"111","author":"Ma","year":"2020","journal-title":"Future Gener. Comput. Syst."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"2823","DOI":"10.1021\/acssensors.4c03583","article-title":"Noninvasive Diagnosis of Early-Stage Chronic Kidney Disease and Monitoring of the Hemodialysis Process in Clinical Practice via Exhaled Breath Analysis Using an Ultrasensitive Flexible NH3 Sensor Assisted by Pattern Recognition","volume":"10","author":"Zhang","year":"2025","journal-title":"ACS Sens."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"4850","DOI":"10.1021\/acssensors.4c03227","article-title":"Disposable Sensor Array Embedded in Facemasks for the Identification of Chronic Kidney Disease","volume":"10","author":"Capuano","year":"2025","journal-title":"ACS Sens."},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Ruini, F., Strano, M., and Mosciatti, T. (2024). Non-Invasive Diagnostic Approaches for Kidney Disease: The Role of Electronic Nose Systems. Sensors, 24.","DOI":"10.3390\/s24196475"},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Yonel, Z., Kuningas, K., and Sharma, P. (2024). Community Point of Care Testing in Diagnosing and Managing Chronic Kidney Disease. Diagnostics, 14.","DOI":"10.3390\/diagnostics14141542"},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Srichan, C., Srichan, W., Danvirutai, P., Ritsongmuang, C., Sharma, A., and Anutrakulchai, S. (2024). Non-Invasive Sensors Integration for NCDs with AIoT Based Telemedicine System. Sensors, 24.","DOI":"10.3390\/s24144431"},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"301","DOI":"10.2215\/CJN.0000000642","article-title":"Unraveling Sex Differences in Kidney Health and CKD: A Review of the Effect of Sex Hormones","volume":"20","author":"Nokoff","year":"2025","journal-title":"Clin. J. Am. Soc. Nephrol."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"649","DOI":"10.1038\/s41371-023-00843-9","article-title":"Sex and Gender Differences in the Management of Chronic Kidney Disease and Hypertension","volume":"37","author":"Mayne","year":"2023","journal-title":"J. Hum. Hypertens."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"47","DOI":"10.1007\/978-3-031-29078-7_5","article-title":"Study on Optimal Machine Learning Approaches for Kidney Failure Detection System Based on Ammonia Level in the Mouth","volume":"1029","author":"Phandinata","year":"2023","journal-title":"Lect. Notes Electr. Eng."},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Kwan, J.A. (2025). Automatic Non-Invasive Kidney Function Detection for Male Patient. Proceedings of the 2025 9th International Conference on Electrical, Electronics and Information Engineering (ICEEIE), IEEE.","DOI":"10.1109\/ICEEIE66203.2025.11252225"},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"625","DOI":"10.1016\/j.transproceed.2016.02.023","article-title":"Comparison between CKD-EPI Creatinine and MDRD Equations to Estimate Glomerular Filtration Rate in Kidney Transplant Patients","volume":"48","author":"Mombelli","year":"2016","journal-title":"Transplant. Proc."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"1737","DOI":"10.1056\/NEJMoa2102953","article-title":"New Creatinine- and Cystatin C-Based Equations to Estimate GFR without Race","volume":"385","author":"Inker","year":"2021","journal-title":"N. Engl. J. Med."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"345","DOI":"10.1007\/s00357-018-9259-9","article-title":"A Comparative Study of Divisive and Agglomerative Hierarchical Clustering Algorithms","volume":"35","author":"Roux","year":"2018","journal-title":"J. Classif."},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Ahmad, I., Alam, M., and Ansari, M.S. (2022). Machine-Learning-Based Disease Diagnosis: A Comprehensive Review. Healthcare, 10.","DOI":"10.3390\/healthcare10030541"},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Winarni, T.I., Haryanto, F., Hartati, S., and Murti, B. (2024). Using Medical Data and Clustering Techniques for a Smart Healthcare System. Electronics, 13.","DOI":"10.3390\/electronics13010140"},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"412","DOI":"10.1016\/j.jpha.2023.02.008","article-title":"Breath-by-Breath Measurement of Exhaled Ammonia by Acetone-Modifier Positive Photoionization Ion Mobility Spectrometry via Online Dilution and Purging Sampling","volume":"13","author":"Wang","year":"2023","journal-title":"J. Pharm. Anal."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"sfag064","DOI":"10.1093\/ckj\/sfag064","article-title":"Breath ammonia test for CKD screening: Impact of fasting on diagnostic accuracy","volume":"19","author":"Chan","year":"2026","journal-title":"Clin. Kidney J."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"2554","DOI":"10.1038\/s41598-018-20503-0","article-title":"Repeated Measures of Blood and Breath Ammonia in Response to Control, Moderate and High Protein Dose in Healthy Men","volume":"8","author":"Spacek","year":"2018","journal-title":"Sci. Rep."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"036002","DOI":"10.1088\/1752-7163\/ab728b","article-title":"Correlation between breath ammonia and blood urea nitrogen levels in CKD and dialysis patients","volume":"14","author":"Chen","year":"2020","journal-title":"J. Breath Res."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"954","DOI":"10.1126\/science.adn6471","article-title":"A smart mask for exhaled breath condensate harvesting and analysis","volume":"385","author":"Heng","year":"2024","journal-title":"Science"},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"328","DOI":"10.1038\/s44460-026-00041-3","article-title":"A battery-free smart mask for long-term exhaled breath biochemical sensing","volume":"1","author":"Heng","year":"2026","journal-title":"Nat. Sens."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"2945","DOI":"10.1021\/acssensors.9b01403","article-title":"Toward continuous monitoring of breath biochemistry","volume":"4","author":"Maier","year":"2019","journal-title":"ACS Sens."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"80","DOI":"10.1038\/s44222-022-00011-7","article-title":"Wearable breath analysis","volume":"1","author":"Ates","year":"2023","journal-title":"Nat. Rev. Bioeng."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"1366","DOI":"10.1038\/s41587-021-00950-3","article-title":"Wearable materials with embedded synthetic biology sensors for biomolecule detection","volume":"39","author":"Nguyen","year":"2021","journal-title":"Nat. Biotechnol."}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/26\/17\/5371\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,8,27]],"date-time":"2026-08-27T04:29:03Z","timestamp":1787804943000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/26\/17\/5371"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,8,25]]},"references-count":41,"journal-issue":{"issue":"17","published-online":{"date-parts":[[2026,9]]}},"alternative-id":["s26175371"],"URL":"https:\/\/doi.org\/10.3390\/s26175371","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,8,25]]}}}