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Preventive healthcare policies in the US: solutions for disease management using
Big Data Analytics
#MMPMID32834926
Batarseh FA
; Ghassib I
; Chong DS
; Su PH
J Big Data
2020[]; 7
(1
): 38
PMID32834926
show ga
Data-driven healthcare policy discussions are gaining traction after the Covid-19
outbreak and ahead of the 2020 US presidential elections. The US has a hybrid
healthcare structure; it is a system that does not provide universal coverage,
albeit few years ago enacted a mandate (Affordable Care Act-ACA) that provides
coverage for the majority of Americans. The US has the highest health expenditure
per capita of all western and developed countries; however, most Americans don't
tap into the benefits of preventive healthcare. It is estimated that only 8% of
Americans undergo routine preventive screenings. On a national level, very few
states (15 out of the 50) have above-average preventive healthcare metrics. In
literature, many studies focus on the cure of diseases (research areas such as
drug discovery and disease prediction); whilst a minority have examined
data-driven preventive measures-a matter that Americans and policy makers ought
to place at the forefront of national issues. In this work, we present solutions
for preventive practices and policies through Machine Learning (ML) methods. ML
is morally neutral, it depends on the data that train the models; in this work,
we make the case that Big Data is an imperative paradigm for healthcare. We
examine disparities in clinical data for US patients by developing correlation
and imputation methods for data completeness. Non-conventional patterns are
identified. The data lifecycle followed is methodical and deliberate;
1000+?clinical, demographical, and laboratory variables are collected from the
Centers for Disease Control and Prevention (CDC). Multiple statistical models are
deployed (Pearson correlations, Cramer's V, MICE, and ANOVA). Other unsupervised
ML models are also examined (K-modes and K-prototypes for clustering). Through
the results presented in the paper, pointers to preventive chronic disease tests
are presented, and the models are tested and evaluated.
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