@article{(Open Science Index):https://publications.waset.org/pdf/10013217,
	  title     = {Comparison of Bayesian and Regression Schemes to Model Public Health Services},
	  author    = {Sotirios Raptis},
	  country	= {},
	  institution	= {},
	  abstract     = {Bayesian reasoning (BR) or Linear (Auto) Regression (AR/LR) can predict different sources of data using priors or other data, and can link social service demands in cohorts, while their consideration in isolation (self-prediction) may lead to service misuse ignoring the context. The paper advocates that BR with Binomial (BD), or Normal (ND) models or raw data (.D) as probabilistic updates can be compared to AR/LR to link services in Scotland and reduce cost by sharing healthcare (HC) resources. Clustering, cross-correlation, along with BR, LR, AR can better predict demand. Insurance companies and policymakers can link such services, and examples include those offered to the elderly, and low-income people, smoking-related services linked to mental health services, or epidemiological weight in children. 22 service packs are used that are published by Public Health Services (PHS) Scotland and Scottish Government (SG) from 1981 to 2019, broken into 110 year series (factors), joined using LR, AR, BR. The Primary component analysis found 11 significant factors, while C-Means (CM) clustering gave five major clusters.},
	    journal   = {International Journal of Mathematical and Computational Sciences},
	  volume    = {17},
	  number    = {8},
	  year      = {2023},
	  pages     = {94 - 107},
	  ee        = {https://publications.waset.org/pdf/10013217},
	  url   	= {https://publications.waset.org/vol/200},
	  bibsource = {https://publications.waset.org/},
	  issn  	= {eISSN: 1307-6892},
	  publisher = {World Academy of Science, Engineering and Technology},
	  index 	= {Open Science Index 200, 2023},
	}