Modeling Cash Conversion Cycle on Hospital Performance with Machine Learning Feature Selection Techniques: Subset Selection and Shrinkage Methods

Richmond Essieku¹ , Solomon Eshun² , Eric Ayamga³ , Prince Bosompim⁴ , James Ladzekpo⁵ , Makafui Komla Akatu⁶

Section:Research Paper, Product Type: Journal-Paper
Vol.10 , Issue.4 , pp.14-22, Aug-2023

Online published on Aug 31, 2023

Copyright © Richmond Essieku, Solomon Eshun, Eric Ayamga, Prince Bosompim, James Ladzekpo, Makafui Komla Akatu . This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
 

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Abstract :
In data science, model selection instability is a major concern, especially when dealing with a high number of features. This paper seeks to contribute by empirically modeling the cash conversion cycle on hospital performance using two supervised machine learning feature selection techniques. We employed methods such as best subset selection coupled with an exhaustive search using linear regression and shrinkage methods (Lasso, Ridge, and ElasticNet) to model a real dataset. The empirical results indicated that the Lasso outperformed the other shrinkage methods in feature selection even though the average root mean squared error (rmse) were close. Again, Account Receivable Days (ARD), Account Payable Days (APD), Inventory Turnover Days (INV), and Debt Ratio were found to be predictors of hospital performance, which are components of the Cash Conversion Cycle. Finally, the results show that, on average, a day decrease in the hospital’s collection period will decrease performance by 1%, and a one-unit increase in a day in the account payable decrease performance by 0.003 times.

Key-Words / Index Term :
Cash Conversion Cycle, Machine Learning, Modeling, Subset Selection, Shrinkage Methods, Hospital performance

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