A top-down scheme of descriptive time series data analysis for healthy life: Introducing a fuzzy amended interaction network

Not only networks are ubiquitous in real world, but also networked dynamics provide a more precise scheme required to better understanding of surrounding phenomena and data. This network-centric approach can be applied to analyze time series data of any type. An abundant prevalence of time series observations demand inference of causality in addition to accurate prediction. In this paper, a fuzzy improved interaction network underlying generalized Lotka-Volterra dynamics is introduced and referred to as FuzzIN. FuzzIN offers a top-down method to predict and describe potential connectivity information embedded in time series. Using FuzzIN, the current paper tries to study the effects of healthcare systems in population health across 21 OECD countries between 1999 and 2012 via OECD Health Data. It is shown that FuzzIN performs well due to its capability of handling nonlinearities, complex interconnectivities and uncertainties in the observed data and excels compared statistical methods. Hence, the relationships are inferred and healthcare systems' performance is discussed by FuzzIN parameters and rules. These estimates can be used to highlight health indicators and problems and to make awareness of development and implementation of effective, targeted public health policies and activities.