Predictive data modeling of human type II diabetes related statistics

Kristina L. Jaenisch R.N.; Holger M. Jaenisch; James W. Handley; Nathaniel G. Albritton

doi:10.1117/12.817874

19 March 2009 Predictive data modeling of human type II diabetes related statistics

Kristina L. Jaenisch R.N., Holger M. Jaenisch, James W. Handley, Nathaniel G. Albritton

Proceedings Volume 7343, Independent Component Analyses, Wavelets, Neural Networks, Biosystems, and Nanoengineering VII; 73431G (2009) https://doi.org/10.1117/12.817874
Event: SPIE Defense, Security, and Sensing, 2009, Orlando, Florida, United States

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.

Citation Download Citation

Kristina L. Jaenisch R.N., Holger M. Jaenisch, James W. Handley, and Nathaniel G. Albritton "Predictive data modeling of human type II diabetes related statistics", Proc. SPIE 7343, Independent Component Analyses, Wavelets, Neural Networks, Biosystems, and Nanoengineering VII, 73431G (19 March 2009); https://doi.org/10.1117/12.817874

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