Cardiovascular diseases are the main cause of mortality in the world. As more people suffer from diabetes and hypertension, the risk of cardiovascular disease (CVD) increases. A sedentary lifestyle, an unhealthy diet, and stressful activities are behaviors that can be changed to prevent CVD. Taking measures to prevent CVD lowers the cost of treatments and reduces mortality. Data-driven plans generate more effective results and can be applied to groups with similar characteristics. Currently, there are several databases that can be used to extract information in real time and improve decision making. This article proposes a methodology for the detection of CVD and a web tool to analyze the data more effectively. The methodology for extracting, describing, and visualizing data from a state-level case study of CVD in Mexico is presented. The data is obtained from the databases of the National Institute of Statistics and Geography (INEGI) and the National Survey of Health and Nutrition (ENSANUT). A k-nearest neighbor (KNN) algorithm is proposed to predict missing data.
Rural economy has been characterized by low incomes and self-consumption production. With the expansion of global markets and the access of customers through internet, the possibility of moving traditional markets to e-commerce increases. This expansion allows the inclusion of rural economy into the e-commerce market. This chapter describes the challenges to be overtaken in order to activate rural economy through e-commerce. The chapter is organized in five sections: The first section focuses on the communication infrastructure available in rural areas. In the second section, the current state and challenges to be addressed for guaranteeing on time delivery are presented. The third section describes payment methods. The fourth section presents schemes of organization required into the communities to guarantee the quality of products. Finally, marketing, advertising, and social networks are discussed.
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