Ensemble Learning Regression Method for Glucose Concentration Prediction System using Colorimetric Paper-based and Smartphones

Dian Wulan Hastuti, Adhi Harmoko Saputro, Cuk Imawan


Prediction of glucose concentration on android smartphones and colorimetric paper-based using the Ensemble learning regression model has been successfully developed. Several successful developments in our research include automatic image segmentation, image correction using the RPCC method, and the development of a regression model for urine glucose predictions. Furthermore, the model was successfully validated for best performance in the respondent's urine susceptible to color change. We used artificial urine at a 0–2000 mg/dl concentration to create a regression model based on Ensemble learning with the boosting optimization method. In addition, we also compared the Ensemble Bagging regression model and the single learner model, Decision Tree. Server-based applications were also developed using RESTful API communication with two servers: an upload server using Node.js and a computing server using the MATLAB Production Server. The testing process results using artificial urine samples showed that the performance of R2 and RRMSE were 0.98 and 0.05 for the Decision Tree and Ensemble Bagging regression models, respectively. While for the Ensemble Boosting regression model, R2 and RRMSE at the testing process are 0.98 and 0.04. The best validation results using respondents' urine samples are shown in the Ensemble Boosting regression model with R2 and RRMSE performance values of 0.97 and 0.06, respectively. The success rate of the application was 100% on both the Samsung Galaxy A51 and Huawei Nova 5T. This research estimated the glucose concentration reasonably well for health monitoring applications.


Ensemble; glucose; MATLAB production server; RESTful API; RPCC; urine.

Full Text:



A. Listyarini, W. Sholihah, and C. Imawan, “A paper-based Colorimetric Indicator Label using Natural Dye for Monitoring Shrimp Spoilage,†in IOP Conference Series: Materials Science and Engineering, 2018, vol. 367, no. 1. doi: 10.1088/1757-899X/367/1/012045. url: https://www.scopus.com/inward/record.uri?eid=2-s2.0-85049373519&doi=10.1088%2F1757-899X%2F367%2F1%2F012045&partnerID=40&md5=21cf99e920aca2161e81b91cd87ff65e.

S. Singh, K. K. Gaikwad, M. Lee, and Y. S. Lee, “Temperature sensitive smart packaging for monitoring the shelf life of fresh beef,†2018, doi: 10.1016/j.jfoodeng.2018.04.014. url: https://doi.org/10.1016/j.jfoodeng.2018.04.014.

I. Hussain, M. Das, K. U. Ahamad, and P. Nath, “Water salinity detection using a smartphone,†Sensors Actuators, B Chem., vol. 239, pp. 1042–1050, 2017, doi: 10.1016/j.snb.2016.08.102. url: http://dx.doi.org/10.1016/j.snb.2016.08.102.

J.-C. Yan, J. Ren, L.-L. Ren, Y. Yang, S.-F. Yang, and T.-L. Ren, “A novel structure design and fabrication method for low liquid consumption and high precision device of colorimeter in water quality detection,†Sensors Actuators A, vol. 289, pp. 1–10, 2019, doi: 10.1016/j.sna.2019.02.016. url: https://doi.org/10.1016/j.sna.2019.02.016.

J.-C. Yan et al., “Development of a portable setup using a miniaturized and high precision colorimeter for the estimation of phosphate in natural water,†2019, doi: 10.1016/j.aca.2019.01.030. url: https://doi.org/10.1016/j.aca.2019.01.030.

E. H. K. Alkamil, S. Al-Dabooni, A. K. Abbas, R. Flori, and D. C. Wunsch, “Learning from experience: An automatic pH neutralization system using hybrid fuzzy system and neural network,†Procedia Comput. Sci., vol. 140, pp. 206–215, 2018, doi: 10.1016/j.procs.2018.10.330. url: https://doi.org/10.1016/j.procs.2018.10.330.

N. Asthana and R. Bahl, “IoT Device for Sewage Gas Monitoring and Alert System,†Proc. 1st Int. Conf. Innov. Inf. Commun. Technol. ICIICT 2019, 2019, doi: 10.1109/ICIICT1.2019.8741423.

M. Carrozzo et al., “UAV intelligent chemical multisensor payload for networked and impromptu gas monitoring tasks,†5th IEEE Int. Work. Metrol. AeroSpace, Metroaerosp. 2018 - Proc., pp. 112–116, 2018, doi: 10.1109/MetroAeroSpace.2018.8453543.

C. Liu, F. A. Gomez, Y. Miao, P. Cui, and W. Lee, “A colorimetric assay system for dopamine using microfluidic paper-based analytical devices,†Talanta, vol. 194. pp. 171–176, 2019. doi: 10.1016/j.talanta.2018.10.039.

K. R. Mallires, D. Wang, P. Wiktor, and N. Tao, “A Microdroplet-Based Colorimetric Sensing Platform on a CMOS Imager Chip,†Anal. Chem., vol. 92, no. 13, pp. 9362–9369, Jul. 2020, doi: 10.1021/acs.analchem.0c01751. url: https://doi.org/10.1021/acs.analchem.0c01751.

T. Akyazi, L. Basabe-Desmonts, and F. Benito-Lopez, “Review on microfluidic paper-based analytical devices towards commercialisation,†Anal. Chim. Acta, vol. 1001, pp. 1–17, Feb. 2018, doi: 10.1016/J.ACA.2017.11.010.

L. M. Fu and Y. N. Wang, “Detection methods and applications of microfluidic paper-based analytical devices,†TrAC - Trends in Analytical Chemistry, vol. 107. pp. 196–211, 2018. doi: 10.1016/j.trac.2018.08.018.

M. I. G. S. Almeida, B. M. Jayawardane, S. D. Kolev, and I. D. McKelvie, “Developments of microfluidic paper-based analytical devices (μPADs) for water analysis: A review,†Talanta, vol. 177, pp. 176–190, Jan. 2018, doi: 10.1016/J.TALANTA.2017.08.072.

G. M. Fernandes et al., “Novel approaches for colorimetric measurements in analytical chemistry – A review,†Analytica Chimica Acta, vol. 1135. pp. 187–203, 2020. doi: 10.1016/j.aca.2020.07.030.

J. Wu, M. Dong, C. Rigatto, Y. Liu, and F. Lin, “Lab-on-chip technology for chronic disease diagnosis,†npj Digit. Med., vol. 1, no. 1, pp. 1–11, 2018, doi: 10.1038/s41746-017-0014-0.

M. Salve, A. Wadafale, G. Dindorkar, and J. Kalambe, “Quantifying colorimetric assays in µPAD for milk adulterants detection using colorimetric android application,†Micro Nano Lett., vol. 13, no. 11, pp. 1520–1524, 2018, doi: 10.1049/mnl.2018.5334.

M. Ra, M. S. Muhammad, C. Lim, S. Han, C. Jung, and W. Y. Kim, “Smartphone-Based Point-of-Care Urinalysis under Variable Illumination,†IEEE J. Transl. Eng. Heal. Med., vol. 6, no. December 2017, pp. 1–11, 2018, doi: 10.1109/JTEHM.2017.2765631.

M. Oyaert and J. R. Delanghe, “Semiquantitative, fully automated urine test strip analysis,†J. Clin. Lab. Anal., vol. 33, no. 5, pp. 1–7, 2019, doi: 10.1002/jcla.22870.

M. Anthimopoulos, S. Gupta, S. Arampatzis, and S. Mougiakakou, “Smartphone-based urine strip analysis,†IST 2016 - 2016 IEEE Int. Conf. Imaging Syst. Tech. Proc., pp. 368–372, 2016, doi: 10.1109/IST.2016.7738253.

M. Jalal Uddin, G. J. Jin, and J. S. Shim, “Paper-Plastic Hybrid Microfluidic Device for Smartphone-Based Colorimetric Analysis of Urine,†Anal. Chem., vol. 89, no. 24, pp. 13160–13166, 2017, doi: 10.1021/acs.analchem.7b02612.

J. Guo, X. Huang, and X. Ma, “Clinical identification of diabetic ketosis/diabetic ketoacidosis acid by electrochemical dual channel test strip with medical smartphone,†Sensors Actuators, B Chem., vol. 275, no. August, pp. 446–450, 2018, doi: 10.1016/j.snb.2018.08.042. url: https://doi.org/10.1016/j.snb.2018.08.042.

S. Schaefer, “Colorimetric water quality sensing with mobile smart phones,†no. April, p. 88, 2014, doi: 10.14288/1.0074338.

M. Spaanderman, R. L. Smeets, P. J. F. Lucas, M. Velikova, and J. T. van Scheltinga, “Smartphone-based analysis of biochemical tests for health monitoring support at home,†Healthc. Technol. Lett., vol. 1, no. 3, pp. 92–97, 2014, doi: 10.1049/htl.2014.0059.

J. Doupis, G. Festas, C. Tsilivigos, V. Efthymiou, and A. Kokkinos, “Smartphone-Based Technology in Diabetes Management,†Diabetes Ther., vol. 11, no. 3, pp. 607–619, 2020, doi: 10.1007/s13300-020-00768-3. url: https://doi.org/10.1007/s13300-020-00768-3.

World Health Organization. (‎2016)‎. Global report on diabetes. World Health Organization. https://apps.who.int/iris/handle/10665/204871.

Infodatin, “Hari Diabetes Sedunia Tahun 2018,†Pus. Data dan Inf. Kementrian Kesehat. RI, pp. 1–8, 2019.

“Konica minolta.†https://sensing.konicaminolta.asia/color-measurement/ (accessed Jul. 21, 2020). url: https://sensing.konicaminolta.asia/color-measurement/.

“clinitek atlas auto urine chem analyzer rack.†https://www.siemens-healthineers.com/it/urinalysis/systems/clinitek-atlas-auto-urine-chem-analyzer-rack (accessed Feb. 16, 2020). url: https://www.siemens-healthineers.com/it/urinalysis/systems/clinitek-atlas-auto-urine-chem-analyzer-rack.

J. Il Hong and B. Y. Chang, “Development of the smartphone-based colorimetry for multi-analyte sensing arrays,†Lab Chip, vol. 14, no. 10, pp. 1725–1732, 2014, doi: 10.1039/c3lc51451j.

P. C. Lin et al., “A machine learning approach for predicting urine output after fluid administration,†Computer Methods and Programs in Biomedicine, vol. 177. pp. 155–159, 2019. doi: 10.1016/j.cmpb.2019.05.009.

A. A. H. Gadalla et al., “Identification of clinical and urine biomarkers for uncomplicated urinary tract infection using machine learning algorithms,†Sci. Rep., vol. 9, no. 1, pp. 1–11, 2019, doi: 10.1038/s41598-019-55523-x.

A. Y. Mutlu, V. Kiliç, G. K. Özdemir, A. Bayram, N. Horzum, and M. E. Solmaz, “Smartphone-based colorimetric detection: Via machine learning,†Analyst, vol. 142, no. 13, pp. 2434–2441, 2017, doi: 10.1039/c7an00741h.

D. Wulan Hastuti, M. Harahap, F. Adila Ferdiansyah, A. Harmoko Saputro, and C. Imawan, “Prediction system for pH measurement on Brassica oleraceae (Red Cabbage) using machine learning regression,†J. Phys. Conf. Ser., vol. 1528, no. 1, 2020, doi: 10.1088/1742-6596/1528/1/012050.

K. Rathan, S. V Sai, and T. Manikanta, “Crypto-Currency price prediction using Decision Tree and Regression techniques,†2019 3rd Int. Conf. Trends Electron. Informatics, pp. 190–194, 2019.

A. M. Shabut et al., “An intelligent mobile-enabled expert system for tuberculosis disease diagnosis in real time,†Expert Syst. Appl., vol. 114, pp. 65–77, 2018, doi: 10.1016/j.eswa.2018.07.014. url: https://doi.org/10.1016/j.eswa.2018.07.014.

D. O. Oyewola, E. G. Dada, O. T. Omotehinwa, and I. A. Ibrahim, “Comparative Analysis of Linear, Non Linear and Ensemble Machine Learning Comparative Analysis of Linear , Non Linear and Ensemble Machine Learning Algorithms for Credit Worthiness of Consumers,†no. September, 2019.

S. Yang, J. Wu, Y. Du, Y. He, and X. Chen, “Ensemble Learning for Short-Term Traffic Prediction Based on Gradient Boosting Machine,†J. Sensors, vol. 2017, 2017, doi: 10.1155/2017/7074143.

A. Mosavi, F. Sajedi Hosseini, B. Choubin, M. Goodarzi, A. A. Dineva, and E. Rafiei Sardooi, “Ensemble Boosting and Bagging Based Machine Learning Models for Groundwater Potential Prediction,†Water Resour. Manag., vol. 35, no. 1, pp. 23–37, 2021, doi: 10.1007/s11269-020-02704-3.

Y. Lu, X. Li, Z. Gong, L. Zhuo, and H. Zhang, “TDCCN: A two-phase deep color correction network for Traditional Chinese Medicine tongue images,†Appl. Sci., vol. 10, no. 5, pp. 1–21, 2020, doi: 10.3390/app10051784.

P. D. Marrero Fernández, F. A. Guerrero Peña, T. Ing Ren, and J. J. G. Leandro, “Fast and robust multiple ColorChecker detection using deep convolutional neural networks,†Image Vis. Comput., vol. 81, pp. 15–24, Jan. 2019, doi: 10.1016/J.IMAVIS.2018.11.001.

N. Promphet et al., “Non-invasive textile based colorimetric sensor for the simultaneous detection of sweat pH and lactate,†Talanta, vol. 192, no. September 2018, pp. 424–430, 2019, doi: 10.1016/j.talanta.2018.09.086. url: https://doi.org/10.1016/j.talanta.2018.09.086.

P. T. Wang, J. J. Chou, and C. W. Tseng, “Colorimetric characterization of color image sensors based on convolutional neural network modeling,†Sensors Mater., vol. 31, no. 5, pp. 1513–1522, 2019, doi: 10.18494/SAM.2019.2271.

S. Perumal and T. Velmurugan, “Preprocessing by Contrast Enhancement Techniques for Medical Images,†Int. J. Pure Appl. Math., vol. 118, no. 18, pp. 3681–3688, 2018.

C. S. Wang et al., “Development of a novel mobile application to detect urine protein for nephrotic syndrome disease monitoring,†BMC Med. Inform. Decis. Mak., vol. 19, no. 1, pp. 1–8, 2019, doi: 10.1186/s12911-019-0822-z.

DOI: http://dx.doi.org/10.18517/ijaseit.12.3.15825


  • There are currently no refbacks.

Published by INSIGHT - Indonesian Society for Knowledge and Human Development