Application of the Mahalanobis-Taguchi System in Renal Profile of the Methadone Flexi Dispensing Program


  • Siti Khadijah Mat Saad Universiti Malaysia Pahang
  • Sri Nur Areena Mohd Zaini Universiti Malaysia Pahang
  • Mohd Yazid Abu Universiti Malaysia Pahang



Mahalanobis-Taguchi system, Mahalanobis distance, renal profile, classification, optimization, methadone flexi dispensing program


Patients under the methadone Flexi dispensing (MFlex) program are required to do blood tests like renal profile. To ensure the patient has a kidney failure, a doctor assesses one parameter like creatinine. Unfortunately, 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 the Mahalanobis-Taguchi system (MTS) in the MFlex program. The data is collected at Bandar Pekan clinic with 34 parameters. Two types of MTS methods are used, such as RT-Method and T-Method, for classification and optimization. As a result, the RT-Method can classify healthy and unhealthy samples, while the T-Method can evaluate the significant parameters in terms of the degree of contribution. Fifteen unknown samples have been diagnosed with different positive and negative degrees of contribution to achieving lower MD. The best-proposed solution is type 5 of 6 modifications because it shows the highest MD value than others. In conclusion, a pharmacist from Bandar Pekan clinic confirmed that MTS could solve a problem in the classification and optimization of the MFlex program.


Bewley-Taylor, D. R., and Nougier, M., Measuring the World Drug Problem: 2019 and Beyond, in K. Axel & S. Blaine (Eds.), Collapse of the Global Order on Drugs: From UNGASS 2016 to Review 2019, 2018, pp. 65-83.

Lian, T. C., and Chu, F. Y., A Qualitative Study on Drug Abuse Relapse in Malaysia: Contributory Factors and Treatment Effectiveness, International Journal of Collaborative Research on Internal Medicine & Public Health, 5(4), 2013, pp. 16.

Ministry, Malaysia Country Report on Drug Issues 2019, Alternative Development towards a Drug-Free ASEAN Community, 2019, pp. 1-27.

Yuswan, F., and Dazali, M. N. M., Policies and Standard Operating Procedures Methadone Treatment Program, Malaysia, 2016.

Jobi-Taiwo, A. A., and Cudney, E. A., Mahalanobis-Taguchi System for Multiclass Classification of Steel Plates Fault, International Journal of Quality Engineering and Technology, 5, 2015, pp. 25-39.

Lee, Y. C., Hsiao, Y. C., Peng, C. F., Tsai, S. B., Wu, C. H., and Chen, Q., Using Mahalanobis–Taguchi System, Logistic Regression, and Neural Network Method to Evaluate Purchasing Audit Quality, Proceedings of the Institution of Mechanical Engineers, Part B: Journal of Engineering Manufacture, 229(1), 2014, pp. 3-12.

Su, C. T., Mahalanobis-Taguchi System and Its Medical Applications, 2017, pp. 1-5.

Waly, G., WDR 2021_Booklet 4, United Nations : Office on Drugs and Crime, 2021, retrieved from

Elflein, J., Topic: Opioid Use in the U.S., Statista, 2021, retrieved from

Taguchi, G., Taguchi Methods in LSI Fabrication Process, 6th International Workshop on Statis-tical Methodology, 2001, pp. 1-6.

Xiao, X., Fu, D., Shi, Y., and Wen, J., Optimized Mahalanobis-Taguchi System for High-Dimen-sional Small Sample Data Classification, Compu-tational Intelligence and Neuroscience, 2020, pp. 4609423.

Jobi-Taiwo, A. A., Data Classification and Forecasting using the Mahalanobis-Taguchi Method, Masters Theses, 2014, pp. 1-56.

Ghasemi, E., Aaghaie, A., and Cudney, E. A., Mahalanobis Taguchi System: A Review, International Journal of Quality & Reliability Management, 32(3), 2015, pp. 291-307.

Ahn, J., Park, M., Lee, H.-S., Ahn, S. J., Ji, S.-H., Song, K., and Son, B.-S., Covariance Effect Analysis of Similarity Measurement Methods for Early Construction Cost Estimation Using Case-Based Reasoning, Automation in Construction, 81, 2017, pp. 254-266.

Safeiee, F. L. M., and Abu, M. Y., Optimization Using Mahalanobis-Taguchi System for Inductor Component, Journal of Physics: Conference Series, 1529, 2020, pp. 1-7.

Ohkubo, M., and Nagata, Y., Anomaly Detection for Unlabelled Unit Space Using the Mahalanobis Taguchi System, Total Quality Management & Business Excellence, 2019, pp. 1-15.

Chang, Z. P., Li, Y. W., and Fatima, N., A Theoretical Survey on Mahalanobis-Taguchi System, Measurement, 2019, pp. 501-510.

Abu, M.Y., Mohd Nor, E.E., and Abd Rahman, M.S., Costing Improvement of Remanufacturing Crankshaft by Integrating Mahalanobis-Taguchi System and Activity based Costing, IOP Conference Series: Materials Science and Engineering, 342, 2018, pp. 1-10.

Buenviaje, B., Bischoff, J., Roncace, R., and Willy, C., Mahalanobis-Taguchi System to Identify Preindicators of Delirium in the ICU, IEEE J Biomed Health Inform, 20(4), 2016, pp. 1205-1213.

Wang, N., Wang, Z., Jia, L., Qin, Y., Chen, X., and Zuo, Y., Adaptive Multiclass Mahalanobis Taguchi System for Bearing Fault Diagnosis under Variable Conditions, Sensors (Basel), 19(1), 2018.

Chen, J., Cheng, L., Yu, H., and Hu, S., Rolling Bearing Fault Diagnosis and Health Assessment Using EEMD and the Adjustment Mahalanobis–Taguchi system, International Journal of Systems Science, 49(1), 2018, pp. 147-159.

El-Banna, M., Modified Mahalanobis Taguchi System for Imbalance Data Classification, Com-pu¬tational Intelligence and Neuroscience, 2017, pp. 1-15.

Mota-Gutiérrez, C. G., Reséndiz-Flores, E. O., and Reyes-Carlos, Y. I., Mahalanobis-Taguchi Sys¬tem: State of the Art, International Journal of Quality & Reliability Management, 35(3), 2018, pp. 596-613.

Rizal, M., Ghani, J. A., Nuawi, M. Z., and Haron, C. H. C., Cutting Tool Wear Classification and Detection Using Multi-sensor Signals and Mahalanobis-Taguchi System, Wear 376-377, 2017, pp. 1759-1765.

Jiangtao, R., Yuanwen, C., and Xiaochen, X., Application of Hilbert-Huang Transform and Mahalanobis-Taguchi System in Mechanical Fault Diagnostics Using Vibration Signals, The Tenth International Conference on Electronic Measurement & Instruments, 2011, pp. 299-303.

Sikder, S., Panja, S. C., and Mukherjee, I., An Integrated Approach for Multivariate Statistical Process Control Using Mahalanobis-Taguchi System and Andrews Function, International Journal of Quality & Reliability Management, 34(8), 2017, pp. 1186-1208.

Wang, N., Saygin, C., and Sun, S.-D., Impact of Mahalanobis Space Construction on Effective¬ness of Mahalanobis–Taguchi System, International Journal of Industrial and Systems Engineering, 13(2), 2013, pp. 233-249.

Abu, M. Y., Jamaludin, K. R., and Ramlie F., Pattern Recognition using Mahalanobis-Taguchi System on Connecting Rod through Remanu-facturing Process: A Case Study, 1st International Materials, Industrial, and Manufacturing Conference, 845, 2014, pp. 584-589.

Abu, M. Y., and Jamaludin, K. R., Application of Mahalanobis-Taguchi System on Crankshaft as Remanufacturing Automotive Part: A Case Study, 1st International Materials, Industrial, and Manufacturing Conference, 845, 2014, pp. 883-888.

Abu, M. Y., Jamaludin, K. R., Md Shaharoun, A., and Emelia Sari., Pattern Recognition on Rema-nufacturing Automotive Component as Support Decision Making Using Mahalanobis-Taguchi System, 12th Global Conference on Sustainable Manufacturing, 26, 2015, pp. 258-263.

Abu, M. Y., Jamaluddin, K. R., and Zakaria, M. A., Classification of Crankshaft Remanufacturing Using Mahalanobis-Taguchi System, International Journal of Automotive and Mechanical Engi-neering, 13(2), 2017, pp. 3413-3422.

Nik Mohd Kamil, N. N., and Abu, M. Y., Integration of Mahalanobis-Taguchi System and Activity Based Costing for Remanufacturing Decision, Journal of Modern Manufacturing Sys-tems and Technology, 1, 2018, pp. 39-51.

Abu, M. Y., Nor, E. E. M., and Rahman, M. S. A., Costing Improvement of Remanufacturing Crankshaft by Integrating Mahalanobis-Taguchi System and Activity based Costing, IOP Confe-rence Series: Materials Science and Engineering, 342, 2018, pp. 1-10.

Abu, M. Y., Norizan, N. S., and Abd Rahman, M. S., Integration of Mahalanobis-Taguchi System and Traditional Cost Accounting for Remanufacturing Crankshaft, IOP Conference Series: Materials Science and Engineering, 342, 2018, pp. 1-9.

Azmi, I. I., Zaini, S. N. A. M., and Abu, M. Y., Application of Mahalanobis-Taguchi System in Palm Oil Plantation, Journal of Modern Manufacturing Systems and Technology, 3, 2019, pp. 1-8.

Nik Mohd Kamil, N. N., Abu, M. Y., Oktaviandri, M., Zamrud, N. F., and Mohd Safeiee, F. L., Application of Mahalanobis-Taguchi System on Electrical and Electronic Industries, Journal of Physics: 4th International Conference on Engi-neer¬ing Technology, 1532, 2020, pp. 1-10.

Nik Mohd Kamil, N. N., Abu, M. Y., Zamrud, N. F., Mohd Safeiee, F. L., Proposing of Mahalanobis-Taguchi System and Time-Driven Activity-Based Costing on Magnetic Component of Electrical & Electronic Industry, Proceedings of the International Manufacturing Engineering Conference & The Asia Pacific Conference on Manufacturing Systems 2019, 2020, pp.108-114.

Kamil, N. N. N. M., Zaini, S. N. A. M., and Abu, M. Y., A Case Study on the Application of Mahalanobis-Taguchi System for Magnetic Component, International Journal of Engineering Technology and Science, 7(2), 2021, pp. 1-12.

Kamil, N. N. N. M., Zaini, S. N. A. M., and Abu, M. Y., Feasibility Study on the Implementation of Mahalanobis Taguchi System and Time Driven Activity Based Costing in Electronic Industry, International Journal of Industrial Management, 10(1), 2021, pp. 160-172.

Saad, S. K. M., Razali, M. H. M., Abu, M. Y., Ramlie, F., Harudin, N., Muhamad, W. Z. A. W., and Dolah, R., Optimizing the MFlex Monitoring System Using Mahalanobis-Taguchi System, IOP Conf. Series: Materials Science and Engineering, 1092, 2021, pp. 1-10.

Ramlie, F., Muhamad, W. Z. A. W., Harudin, N., Abu, M. Y., Yahaya, H., Jamaludin, K. R., and Abdul Talib, H. H., Classification Performance of Thresholding Methods in the Mahalanobis–Taguchi System, Applied Sciences, 11, 2021, pp. 1-22.

Harudin, N., Ramlie, F., Wan Muhamad, W. Z. A., Muhtazaruddin, M. N., Jamaludin, K. R., Abu, M. Y., and Marlan, Z. M., Binary Bitwise Artificial Bee Colony as Feature Selection Optimization Approach within Taguchi’s T-method, Mathematical Problems in Engineering, 2021, pp. 1-10.

Teshima, S., Hasegawa, Y., and Tatebayashi, K., Quality Recognition and Prediction: Smarter Pattern Technology with the Mahalanobis-Taguchi System Momentum Press LLC, 2012, pp. 1-220.