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# Skyler Kuhn
# Center for the Study of Biological Complexity
# Biodemography Project: Apriori Aligorithm
# Version 1.2.0
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Biodemography | Apriori Algorithm | Data Mining | Combinatorial optimization | WHO

Usage Requirements:   
--- Data files should be in csv format      
--- Required External Packages: [scipy.stats, numpy]  

This package implements an unique method for utilizing frequent item-set data mining techniques 
in exhaustive combinatorics to find conserved patterns in the different global causes of mortality. 
A modified version of the apriori algorithm is implemented to find the common causes mortality
across the globe in a set of 35 icd codes. The theory being that once all of the conserved possible 
combinations leading to morbidity across the globe are determined, one may tentatively identify whether 
the underlying etiologies of these diseases or groups of diseases are genetic or environmental. Overall,   
this work introduces a practical framework to find conserved patterns in the different causes of 
mortality across the globe.

Understanding Mortality and Aging
Center for the Study of Biological Complexity & Department of Computer Science
Labs of Dr. Tarynn Witten and Dr. Alberto