Application of Mahalanobis-Taguchi system in descending case of methadone flexi dispensing (MFlex) program
DOI:
https://doi.org/10.15282/jmmst.v4i2.7035Keywords:
Mahalanobis-Taguchi system, Mahalanobis distance, descending case, methadone flexi dispensing program, classification, optimizationAbstract
Patient under methadone flexi dispensing (MFlex) program is subjected to do methadone dosage trends for descending case since no parameters were employed 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 the 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. In classification of descending case, the average Mahalanobis distance (MD) of healthy is 1.0000 and unhealthy is 11123.9730. In optimization of descending case, there are 9 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 6 of 6 modifications has been selected as the best proposed solution. 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
J. Elflein, "Illegal drug use prevalence global population 2019," Statista, retrieved September 16, 2021, from https://www.statista.com/statistics/274690/population-prevalence-of-illegal-drugs-worldwide-since-1990/.
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.
T. C. Lian, and F. Y. Chu, "A qualitative study on drug abuse relapse in Malaysia: Contributory factors and treatment effectiveness," International Journal of Collaborative Research on Internal Medicine & Public Health, vol. 5, no. 4, pp. 217 - 232, 2013.
Ministry of Health of Malaysia, "Malaysia Country Report on Drug Issues 2019," Alternative Development towards a Drug-Free ASEAN Community, pp 1-27, 2019.
F. Yuswan, and M. N. M. Dazali, "Policies and Standard Operating Procedures Methadone Treatment Program," pp 7-42, 2016.
E. Reséndiz, L. A. Moncayo-Martínez, and G. Solís, "Binary ant colony optimization applied to variable screening in the Mahalanobis–Taguchi System," Expert Systems With Applications, vol. 4, no. 2, pp. 634-637, 2013, doi:10.1016/j.eswa.2012.07.058.
World Drug Report, "World Drug Report 2019: 35 million people worldwide suffer from drug use disorders while only 1 in 7 people receive treatment," United Nations : Office on Drugs and Crime, retrieved September 16, 2021, from https://www.unodc.org/unodc/en/frontpage/2019/June/world-drug-report-2019_-35-million-people-worldwide-suffer-from-drug-use-disorders-while-only-1-in-7-people-receive-treatment.html.
S. K. M. Saad et al., "Optimizing the MFlex monitoring system using Mahalanobis-Taguchi system," IOP Conf. Series: Materials Science and Engineering, vol. 1092, pp. 1-10, 2021, doi:10.1088/1757-899X/1092/1/012009.
G. Taguchi, "Taguchi methods in LSI fabrication process," 6th International Workshop on Statistical Methodology, pp. 1-6, 2001, doi:10.1109/iwstm.2001.933815.
A. A. Jobi-Taiwo, "Data classification and forecasting using the Mahalanobis-Taguchi method," Masters Theses, pp. 1-56, 2012.
W. H. Woodall, R. Koudelik, K. -L. Tsui, S. B. Kim, Z. G. Stoumbos, and C. P. C. MD, "A Review and Analysis of the Mahalanobis-Taguchi System," Technometrics, vol. 45, no. 1, pp. 1-15, 2012, doi:10.1198/004017002188618626.
E. Ghasemi, A. Aaghaie, and E. A. Cudney, "Mahalanobis Taguchi system: a review," International Journal of Quality & Reliability Management, vol. 32, no. 3, pp. 291-307, 2015, doi:10.1108/ijqrm-02-2014-0024.
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.
S. Teshima, Y. Hasegawa, and K. Tatebayashi, "Quality Recognition and Prediction: Smarter Pattern Technology with the Mahalanobis-Taguchi System," Momentum Press LLC, pp. 1-220, 2012, doi10.5643/9781606503447.
F. L. M. Safeiee, and M. Y. Abu, "Optimization using Mahalanobis-Taguchi System for inductor component," Journal of Physics: Conference Series, vol. 1529, pp. 1-7. 2020, doi:10.1088/1742-6596/1529/5/052045.
M. Ohkubo, and Y. Nagata, "Anomaly detection for unlabelled unit space using the Mahalanobis Taguchi system," Total Quality Management & Business Excellence, pp. 1-15, 2019, doi:10.1080/14783363.2019.1616542.
Z. P. Chang, Y. W. Li, and N. Fatima, "A theoretical survey on Mahalanobis-Taguchi system," Measurement, pp. 501-510, 2019, doi:10.1016/j.measurement.2018.12.090.
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.
M. El-Banna, Modified Mahalanobis Taguchi System for Imbalance Data Classification," Computational Intelligence and Neuroscience, pp. 1-15, 2017, doi:10.1155/2017/5874896.
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.
D. C. Montgomery, "The Mahalanobis-Taguchi Strategy," Journal of Quality Technology, vol. 35, no. 2, pp. 229-231, 2018, doi:10.1080/00224065.2003.11980211.
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Copyright (c) 2020 S.N.A.M. Zaini, S.K.M. Saad, M.Y. Abu
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