Self-Organizing Maps of Nutrition, Lifestyle and Health Situation in the World

Abstract

Abstract. In this article, we present an analysis of the impact of nutrition and lifestyle on health at a global level. We have used Self-organizing Maps (SOM) algorithm as the analysis technique. SOM enables us to visualize the relative position of each country against a set of the variables related to nutrition, lifestyle and health. The positioning of the countries follows the basic understanding of their status with respect to their socioeconomic conditions. We have also studied the relationships between the variables supported by the SOM visualization. This analysis presents many obvious correlations but also some surprising findings that are worth further analyses.

Quantitative relationships between investments in farms and environmental effects in some European countries

“…In general, self-organizing maps are particularly useful to estimate, in time series, the structure and the evolution of several variables, such as poverty, lifestyle, health situation, development and welfare features in different countries in order to obtain a unique parameter and a visualization of different clusters of states or rather groups of hexagons with lots of analogies to the principal component analysis (Kasky and Kohonen, 1996;Mehmood et al, 2011). The selforganizing map (SOM) is a particular quantitative model of an artificial neural network able to produce a low two-dimensional representation of inputs in some maps (Kohonen, 1995).…”

Technical and economic efficiency analysis on Italian smallholder family farms using Farm Accountancy Data Network dataset

“…The SOMs are based on a method of unsupervised learning in a restricted space provided that the topological properties of an input space or stimulus come from the outside (Kohonen, 2001). The main benefi t of the SOM approach is to obtain a unique pattern able to classify homogenous groups or clusters, preserving their dissimilarities and, as with Principal Component Analysis, reducing the complexity via a map that highlights the relationships among the variables (Mehmood et al, 2011).…”

Patient level analytics using self-organising maps: A case study on Type-1 Diabetes self-care survey responses

“…Reducing the complexity in the data reveals more meaningful relationships, enabling understanding of the dependencies among the responses given in the survey. Previously, SOM has been used to visually explore data areas such as health, lifestyle, nutrition [14], financial [15], gene expression [2] [16], marine safety [17] and linguistics [18]. Recently, SOM has also been used to explore questionnaire based loneliness survey data [3].…”