Volume 2 Issue 4 | 2025 | View PDF
Paper Id:IJMSM-V2I4P102
doi: 10.71141/30485037/V2I4P102

Predicting Medication-Related Risk in Chronic Disease Management Using Machine Learning

Blessing Malcolm Awukam, Chinaza Felicia Nwakobe, Sunday Okafor

Citation:
Blessing Malcolm Awukam, Chinaza Felicia Nwakobe, Sunday Okafor, "Predicting Medication-Related Risk in Chronic Disease Management Using Machine Learning" International Journal of Multidisciplinary on Science and Management, Vol. 2, No. 4, pp. 8-21, 2025.

Abstract:
The Pressure Area Method (PAM) is a simplified yet code-recognized analytical technique employed to evaluate the structural adequacy of nozzle penetrations in pressure vessels. Based on a limit load concept, PAM equates the internal pressure force acting on the removed area of the vessel wall to the resisting force provided by the available reinforcement, including both the parent shell and any additional reinforcement such as pads. This study presents a comprehensive overview of the PAM framework, applicable to both cylindrical and spherical shells, and outlines the governing equations for configurations with and without reinforcing pads. A detailed nomenclature and explanation of code-dependent k-factors are provided to support implementation. A worked example involving a flush set-in nozzle with a reinforcing pad in a cylindrical shell is included to demonstrate the methodology’s practical application. Results show that the nozzle intersection governs the Maximum Allowable Working Pressure (MAWP), highlighting the method’s conservative yet effective design approach. While PAM does not explicitly account for stress concentrations or external load effects, it remains a robust, mechanics-based tool widely adopted in pressure vessel design standards. Its straightforward nature makes it highly suitable for spreadsheet-based implementation and early-stage design validation in both European and international contexts.

Keywords: Machine Learning, Predictive Modelling, Medication Safety, Chronic Disease Management, Adverse Drug Events, Polypharmacy, Healthcare Informatics.

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