Application of Mahalanobis-Taguchi system in ascending case of methadone flexi dispensing (MFlex) program
DOI:
https://doi.org/10.15282/jmmst.v4i1.7034Keywords:
Mahalanobis-Taguchi system, Mahalanobis distance, ascending case, methadone flexi dispensing program, classification, optimizationAbstract
Patient under methadone flexi dispensing (MFlex) program is subjected to do methadone dosage trends like ascending case since no parameters have been used to identify the patient who has potential rate of recovery. Consequently, the existing system does not have a stable ecosystem towards classification and optimization due to inaccurate measurement methods and lack of justification of significant parameters which will influence the accuracy of diagnosis. The objective is to apply Mahalanobis-Taguchi system (MTS) in the MFlex program as it has never been done in previous studies. The data is collected at Bandar Pekan clinic with 16 parameters. Two types of MTS methods are used like RT-Method and T-Method for classification and optimization respectively. As a result, RT-Method is able to classify the average Mahalanobis distance (MD) of healthy and unhealthy with 1.0000 and 21387.1249 respectively. Moreover, T-Method is able to evaluate the significant parameters with 10 parameters of positive degree of contribution. 6 unknown samples have been diagnosed using MTS with different number of positive and negative degree of contribution to achieve lower MD. Type 2 of 6 modifications has been selected as the best proposed solution as it shows the lowest positive MD value. In conclusion, a pharmacist from Bandar Pekan clinic has confirmed that MTS is able to solve a problem in classification and optimization of MFlex program.
References
G. Waly, "WDR 2021_booklet 1. United Nations : Office on Drugs and Crime," retrieved 2021, from https://www.unodc.org/unodc/en/data-and-analysis/wdr-2021_booklet-1.html.
A. Peacock, J. Leung, S. Larney, S. Colledge, M. Hickman, J. Rehm, and L. Degenhardt, "Global statistics on alcohol, tobacco and illicit drug use: 2017 status report," Addiction, vol. 113, no. 10, pp. 1905-1926, 2018, doi:10.1111/add.14234.
Ministry of Health of Malaysia, "Malaysia Country Report on Drug Issues 2019," Alternative Development towards a Drug-Free ASEAN Community, pp 1-27, 2019.
Y. Buntat, and D. R. Rahaman, "Addiction profile among addicts detained at CRCC throughout Malaysia," pp 1-19, 2014.
F. Yuswan, and M. N. M. Dazali, "Policies and Standard Operating Procedures Methadone Treatment Program," pp 7-42, 2016.
A. A. Jobi-Taiwo, and E. A. Cudney, "Mahalanobis-Taguchi system for multiclass classification of steel plates fault," Int. J. Quality Engineering and Technology, vol. 5, no. 1, pp. 25-39, 2015, doi:10.1504/IJQET.2015.069231.
Z. P. Chang, Y. W. Li, and N. Fatima, "A theoretical survey on Mahalanobis-Taguchi system," Measurement, vol. 136, pp. 501-510, 2019, doi:10.1016/j.measurement.2018.12.090.
A. A. Jobi-Taiwo, "Data classification and forecasting using the Mahalanobis-Taguchi method," Masters Theses, pp. 1-56, 2012.
X. Xiao, D. Fu, Y. Shi, and J. Wen, "Optimized Mahalanobis-Taguchi System for High-Dimensional Small Sample Data Classification," Computational Intelligence and Neuroscience, 2020, doi:10.1155/2020/4609423.
G. Taguchi, and R. Jugulum, "The Mahalanobis-Taguchi Strategy: A Pattern Technology System", retrieved January 05, 2021, from https://books.google.com.my/books?hl=en.
J. Ahn, M. Park, H. -S. Lee, S. J. Ahn, S. -H. Ji, K. Song, and B. -S. Son, "Covariance effect analysis of similarity measurement methods for early construction cost estimation using case-based reasoning," Automation in Construction, vol. 81, pp. 254-266, 2017, doi:10.1016/j.autcon.2017.04.009.
J. Ahn, M. Park, H. -S. Lee, S. J. Ahn, S. -H. Ji, K. Song, and B. -S. Son, "Covariance effect analysis of similarity measurement methods for early construction cost estimation using case-based reasoning," Automation in Construction, vol. 81, pp. 254-266, 2017, doi:10.1016/j.autcon.2017.04.009.
M. Y. Abu, E. E. M. Nor, and M. S. A. Rahman, "Costing improvement of remanufacturing crankshaft by integrating Mahalanobis-Taguchi System and Activity based Costing," IOP Conference Series: Materials Science and Engineering, vol. 342, pp. 1-10, 2018, doi:10.1088/1757-899x/342/1/012006.
B. Buenviaje, J. Bischoff, R. Roncace, and C. Willy, "Mahalanobis-Taguchi System to Identify Preindicators of Delirium in the ICU," IEEE J Biomed Health Inform, vol. 20, no. 4, pp. 1205-1213, 2016, doi:10.1109/JBHI.2015.2434949.
N. Wang, Z. Wang, L. Jia, Y. Qin, X. Chen, and Y. Zuo, "Adaptive Multiclass Mahalanobis Taguchi System for Bearing Fault Diagnosis under Variable Conditions," Sensors (Basel), vol. 19, no. 1, pp. 1-16, 2018, doi:10.3390/s19010026.
J. Chen, L. Cheng, H. Yu, and S. Hu, "Rolling bearing fault diagnosis and health assessment using EEMD and the adjustment Mahalanobis–Taguchi system," International Journal of Systems Science, vol. 49, no. 1, pp. 147-159, 2018, doi:10.1080/00207721.2017.1397804.
C. G. Mota-Gutiérrez, E. O. Reséndiz-Flores, and Y. I. Reyes-Carlos, "Mahalanobis-Taguchi system: state of the art," International Journal of Quality & Reliability Management, vol. 35, no. 3, pp. 596-613, 2018, doi:10.1108/IJQRM-10-2016-0174.
M. Ohkubo, and Y. Nagata, "Anomaly detection for unlabelled unit space using the Mahalanobis Taguchi system," Total Quality Management & Business Excellence, vol. 32, no. 5-6, pp. 1-15, 2019, doi:10.1080/14783363.2019.1616542.
S. Fukuda, "A Mahalanobis Taguchi Approach to Human Motion Control," Advances in Intelligent Systems and Computing, vol 1, pp. 65-71, 2017, doi:10.1007/978-3-319-60495-4_7.
P. Shakya, M. S. Kulkarni, and A. K. Darpe, "A novel methodology for online detection of bearing health status fornaturally progressing defect," Journal of Sound and Vibration, vol. 333, no. 21, pp. 5614-5629, 2014, doi:10.1016/j.jsv.2014.04.058.
M. Ketkar, and D. O. S. Vaidya, "Evaluating and Ranking Candidates for MBA program: Mahalanobis Taguchi System Approach," Procedia Economics and Finance, vol. 11, pp. 654 – 664, 2014, doi:10.1016/S2212-5671(14)00231-7.
D. Liparas, N. Laskaris, and L. Angelis, "Incorporating resting state dynamics in the analysis of encephalographic responses by means of the Mahalanobis–Taguchi strategy," Expert Systems with Applications: An International Journal, vol. 40, no. 7, pp. 2621–2630, 2013, doi:10.1016/j.eswa.2012.11.014.
M. Y. Abu, K. R. Jamaludin, and F. Ramlie, "Pattern Recognition using Mahalanobis-Taguchi system on Connecting Rod through Remanufacturing Process: A Case Study," 1st International Materials, Industrial, and Manufacturing Conference, vol. 845, pp. 584-589, 2014, doi:10.4028/www.scientific.net/AMR.845.584.
M. Y. Abu, and K. R. Jamaludin, "Application of Mahalanobis-Taguchi System on Crankshaft as Remanufacturing Automotive Part: A Case Study," 1st International Materials, Industrial, and Manufacturing Conference, vol. 845, pp. 883-888, 2014, doi:10.4028 / www.scientific.net/amr.845.883.
M. Y. Abu, K.R. Jamaludin, A. M. Shaharoun, and E. Sari, "Pattern Recognition on Remanufacturing Automotive Component as Support Decision Making using Mahalanobis-Taguchi System," 12th Global Conference on Sustainable Manufacturing, vol. 26, pp. 258-263, 2015, doi:10.1016/j.procir.2014.07.025.
Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2020 S.N.A.M. Zaini, S.K.M. Saad, M.Y. Abu
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
