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Proceedings Paper

Predictive data modeling of human type II diabetes related statistics
Author(s): Kristina L. Jaenisch R.N.; Holger M. Jaenisch; James W. Handley; Nathaniel G. Albritton
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Paper Abstract

During the course of routine Type II treatment of one of the authors, it was decided to derive predictive analytical Data Models of the daily sampled vital statistics: namely weight, blood pressure, and blood sugar, to determine if the covariance among the observed variables could yield a descriptive equation based model, or better still, a predictive analytical model that could forecast the expected future trend of the variables and possibly eliminate the number of finger stickings required to montior blood sugar levels. The personal history and analysis with resulting models are presented.

Paper Details

Date Published: 19 March 2009
PDF: 12 pages
Proc. SPIE 7343, Independent Component Analyses, Wavelets, Neural Networks, Biosystems, and Nanoengineering VII, 73431G (19 March 2009); doi: 10.1117/12.817874
Show Author Affiliations
Kristina L. Jaenisch R.N., Licht Strahl Engineering Inc. (United States)
Holger M. Jaenisch, Licht Strahl Engineering Inc. (United States)
Amtec Corp. (United States)
James W. Handley, Licht Strahl Engineering Inc. (United States)
Amtec Corp. (United States)
Nathaniel G. Albritton, Amtec Corp. (United States)


Published in SPIE Proceedings Vol. 7343:
Independent Component Analyses, Wavelets, Neural Networks, Biosystems, and Nanoengineering VII
Harold H. Szu; F. Jack Agee, Editor(s)

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