The digital divide in Europe has not yet been bridged and thus more contributions towards understanding the factors affecting the different dimensions involved are required. This research offers some insights into the topic by analyzing the e-Government adoption or practical use of e-Government across Europe (26 EU countries). Based on the data provided by the statistical office of the European Union (Eurostat), we defined two indexes, the E-Government Use Index (EGUI) and an extreme version of it taking into account only null or complete use (EGUI⁺), and characterized the use/non use of e-Government tools using supervised learning procedures in a selection of countries with different e-Government adoption levels. These procedures achieved an average accuracy of 73% and determined the main factors related to the practical use of e-Government in each of the countries, e.g. the frequency of buying goods over the Internet or the education level. In addition, we compared the proposed indexes to other indexes measuring the level of e-readiness of a country such as the E-Government Development Index (EGDI) its Online Service Index (OSI) component, the Networked Readiness Index (NRI) and its Government usage component (GU). The ranking comparison found that EGUI⁺ is correlated with the four indexes mentioned at 0.05 significance level, as the majority of countries were ranked in similar positions. The outcomes contribute to gaining understanding about the factors influencing the use of e-Government in Europe and the different adoption levels.

Non-motor manifestations of Parkinson’s disease (PD) appear early and have a significant impact on the quality of life of patients, but few studies have evaluated their predictive potential with machine learning algorithms. We evaluated 9 algorithms for discriminating PD patients from controls using a wide collection of non-motor clinical PD features from two databases: Biocruces (96 subjects) and PPMI (687 subjects). In addition, we evaluated whether the combination of both databases could improve the individual results. For each database 2 versions with different granularity were created and a feature selection process was performed. We observed that most of the algorithms were able to detect PD patients with high accuracy (>80%). Support Vector Machine and Multi-Layer Perceptron obtained the best performance, with an accuracy of 86.3% and 84.7%, respectively. Likewise, feature selection led to a significant reduction in the number of variables and to better performance. Besides, the enrichment of Biocruces database with data from PPMI moderately benefited the performance of the classification algorithms, especially the recall and to a lesser extent the accuracy, while the precision worsened slightly. The use of interpretable rules obtained by the RIPPER algorithm showed that simply using two variables (autonomic manifestations and olfactory dysfunction), it was possible to achieve an accuracy of 84.4%. Our study demonstrates that the analysis of non-motor parameters of PD through machine learning techniques can detect PD patients with high accuracy and recall, and allows us to select the most discriminative non-motor variables to create potential tools for PD screening.