Optimizing the MFlex monitoring system using Mahalanobis-Taguchi system

• Published in: IOP conference series. Materials Science and Engineering Vol. 1092; no. 1; p. 12009
• Main Authors: Saad, S K M, Razali, M H M, Abu, M Y, Ramlie, F, Harudin, N, Muhamad, W Z A W, Dolah, R
• Format: Journal Article
• Language: English
• Published: Bristol IOP Publishing 01.03.2021
• Subjects: Applications programs; Decision making; Graphical user interface; Incentives; Leukocytes; Mathematical analysis; Methadone; Mobile computing; Monitoring; Monitoring systems; Multivariate analysis; Parameters; Performance prediction; Signal to noise ratio; Specific gravity; Taguchi methods
• ISSN: 1757-8981; 1757-899X
• DOI: 10.1088/1757-899X/1092/1/012009

Abstract Methadone Flexi Dispensing Service (MFlex) has been officially re-branded from Methadone 1Malaysia Service (M1M) since 2nd January 2019. Patients under MFlex are frequently taking their methadone according to a plan provided by pharmacist at public clinic. From the dose monitoring taken annually, pharmacists can predict critical patients based on high monthly dose increases. However, the current monitoring system is written documentation with total doses that cannot accurately measure addiction levels and slow down the distribution process to appropriate incentives as provided by the government. The main objective of this work is to develop a new data monitoring system by evaluating all factors contributed to the addiction level. Mahalanobis-Taguchi System (MTS) is a method of predicting and diagnosing system performance using multivariate data in order to make quantitative decisions with the construction of a multivariate measurement scale using an analytical method. The results show that the minimum Mahalanobis Distance (MD) for healthy data is 0.2245 while the maximum is 2.3380. The minimum and maximum MD of unhealthy data is 0.6077 and 24.5719 respectively. Thus, parameters of blood, bilirubin, nitrite, specific gravity, leukocytes are considered as significant parameters by considering positive value signal-to-noise ratio (SNR) gain. Graphical user interface (GUI) has been developed for analyzing the normal and abnormal patients in detail. Meanwhile, mobile application has been developed as a decision-making tool to classify that the patients is either normal or abnormal.