In this article, I estimate the value of “Individual Using Internet”-IUI in the context of Environmental, Social and Governance-ESG database of the World Bank. I use data from 193 countries for the period 2011-2020. I found that among others the value of IUI is positively associated to “Methane Emissions” and “People Using Safely Managed Sanitation Services” and negatively associated among others to “Fossil Fuel Energy Consumption” and “Renewable Energy Consumption”. I apply the k-Means algorithm for the clusterization optimized with the Elbow Method and we find the presence of three clusters. Finally, I confront eight machine-learning algorithms to predict the future value of IUI. I found that the best predictive algorithm is Linear Regression and that the value of IUI is expected to decrease on average of 0.30% for the analysed countries.

In this article, we estimate the value of Government Expenditure on Education-GEE in the context of Environmental, Social and Governance-ESG dataset of the World Bank. We use data from 193 countries in the period 2011-2020. We use Panel Data with Fixed Effects, Panel Data with Random Effects, Pooled Ordinary Least Squares-OLS, and Weighted Least Squares-WLS. Our results show that the value of GEE is positively associated among others to “Case of Death, by communicable disease and maternal, prenatal and nutrition conditions”, and “Unemployment”, and negatively associated among others to “Hospital Beds” and “Government Effectiveness”. Furthermore, we apply the k-Means algorithm optimized with the Elbow Method and we find the presence of four clusters. Finally, we confront eight machine learning algorithms for the prediction of the future value of GEE. We found that the Polynomial Regression is the best predictive algorithm. The Polynomial Regression predicts an increase in GEE of 7.09% on average for the analysed countries.

We analyze the question of GDP Growth-GDPG rate in the context of Environmental, Social and Governance-ESG framework. We use World Bank data for 193 countries in the period 2011-2020 using different econometric techniques i.e., Panel Data with Fixed Effects, Panel Data with Random Effects, Pooled Ordinary Least Squares-OLS. We found that GDPG rate is positively associated, among others, to “Government Effectiveness” and “Prevalence of Undernourishment” and negatively associated among others to “Unemployment” and “Research and Development Expenditure”. Furthermore, we have applied the k-Means algorithm optimized with the Elbow method and we found the presence of four clusters in the sense of GDPG rate. Finally, we confront eight machine learning algorithms to predict the value of GDPG rate and we found that the Polynomial Regression is the best predictor. The Polynomial Regression predicts an increase of GDPG rate equal to 2.88% on average for the analysed countries.

We estimate the determinants of Emissions-COE in the context of Environmental, Social and Governance-ESG model at world level. We use data of the World Bank for 193 countries in the period 2011-2020. We found that the level of COE is positively associated, among others to “Methane Emissions”, “Research and Development Expenditures”, and negatively associated among others to “Renewable Energy Consumption” and “Mean Drought Index”. Furthermore, we have applied a cluster analysis with the k-Means algorithm optimized with the Elbow Method and we find the presence of four cluster. Finally, we apply eight machine-learning algorithms for the prediction of the future value of COE and we find that the Artificial Neural Network-ANN algorithm is the best predictor. The ANN predicts a reduction in the level of COE equal to 5.69% on average for the analysed countries.

We estimate the value of Research and Development Expenditures as a percentage of GDP-RDE in the context of Environmental, Social and Governance-ESG model. We use the ESG World Bank database. We analyze data from193 countries in the period 2011-2020. We apply a set of econometric techniques i.e. Pooled Ordinary Least Squares-OLS, Panel Data with Random Effects, Panel Data with Fixed Effects, Weighted Least Squares-WLS. We found that the level of RDE is positively associated, among others, to “Nitrous Oxide Emissions” and “Scientific and Technical Journal Articles”, and negatively associated, among others to “Heat Index 35”, “Maximum 5-day Rainfall”. Furthermore, we perform a cluster analysis with the application of the k-Means algorithm optimized with the Elbow Method. The results show the presence of four clusters. Finally, we confront eight different machine-learning algorithms to predict the future value of RDE. We find that Linear Regression is the best predictive algorithms. RDE is expected to growth on average of 0.07% for the analysed countries.

In this article, we estimate the role of Political Stability and Absence of Violence and Terrorism-PS in the context of Environmental, Social and Governance-ESG data at world level. We analyse data from 193 countries in the period 2011-2020. We apply Panel Data with Fixed Effects, Panel Data with Random Effects and Pooled Ordinary Least Square-OLS. We found that PS is positively associated, among others, to Population Density and Government Effectiveness, and negatively associated, among others, to Research and Development Expenditure and Maximum 5-day Rainfall. Furthermore, we apply the k-Means algorithm optimized with the application of the Elbow Method and we find the presence of four clusters. Finally, we propose a confrontation among eight different machine-learning algorithms for the prediction of PS and we find that the Polynomial Regression shows the higher performance. The Polynomial Regression predicts an increase in the level of PS of 0.25% on average for the analysed countries.

In this article, we analyse the determinants of Regulatory Quality-RQ for 193 countries in the period 2011-2020. We use a database from ESG-Environment Social Governance of the World Bank. We apply OLS, Panel Data with Fixed Effects and Panel Data with Random Effects. We found that the variables that have the most positive impact on RQ, among others, are “GHG Net Emission”, “Mean Drought Index”, and “Heat Index”. We also found that the variables that have the most negative impact on RQ are among others “Renewable Energy Consumption”, “Voice and Accountability” and “Rule of Law”. Furthermore, we have applied the k-Means algorithm optimized with the Elbow Method and we find the presence of five clusters. In adjunct, we confront eight machine learning algorithms to predict the value of RQ and we found that the best predictor is Polynomial Regression. The predictive level of RQ for the analysed countries is expected to diminish of -1,29%. In the end, we present a network analysis with the Euclidean distance and we found the presence of a structure of seven networks using augmented data.

In the following article, we analyse the determinants of the number of physicians in the context of ISTAT BES-Benessere Equo Sostenibile data among twenty Italian regions in the period 2004-2022. We apply Panel Data with Random Effects, Panel Data with Fixed Effects, and Pooled OLS-Ordinary Least Squares. We found that the number of Physicians among Italian regions is positively associated, among others, to “Trust in the Police and Firefighters”, “Net Income Inequality”, and negatively associated, among others, to “Research and Development Intensity” and “Soil waterproofing by artificial cover”. Furthermore, we apply the k-Means algorithm optimized with the Silhouette Coefficient and we find the presence of two clusters. Finally, we confront eight different machine-learning algorithms to predict the future value of physicians and we find that the PNN-Probabilistic Neural Network is the best predictive algorithm.

In this article we estimate the level of Government Effectiveness-GE in 193 countries in the period 2011-2020 using data of the ESG World Bank Database. Different econometric techniques are used i.e. Panel Data with Random Effects, Panel Data with Fixed Effects, and Pooled OLS. Results show that GE is positively related among others to “Control of Corruption”, “Political Stability and Absence of Violence/Terrorism”, and negatively associated with “Percentage Annual GDP Growth”. We perform a cluster analysis with the k-Means algorithm optimized with the Elbow Method and we find the presence of four clusters. Finally, we confront eight machine learning algorithms for the prediction of GE. Results show that the Polynomial Regression is the best predictive algorithm. The value of GE is expected to growth on average by 15.97%.

In this article we estimate the level of Control of Corruption for 193 countries in the period 2011-2020 using data from the ESG World Bank Database. Various econometric techniques are applied i.e.: Panel Data with Random Effects, Panel Data with Fixed Effects, Pooled OLS, WLS. Results show that “Control of Corruption” is positively associated, among others, to “Government Effectiveness” and “Political Stability and Absence of Violence/Terrorism”, while it is negatively associated among others to “Agriculture, Forestry, and Fishing Value Added as Percentage of GDP” and “GHG Net Emissions/Removals by LUCF”. A cluster analysis implemented with the k-Means algorithm optimized with the Elbow Method shows four clusters. A confrontation among eight Machine Learning algorithms is proposed for the prediction of Control of Corruption. Polynomial Regression is the best predictor for the training data. The level of Control of Corruption is expected to growth by 10.36% on average.

In this article we investigate the role of “Renewable Energy Consumption” in the context of Circular Economy. We use data from the World Bank for 193 countries in the period 2011-2020. We perform several econometric techniques i.e., Panel Data with Fixed Effects, Panel Data with Random Effects, Pooled OLS, WLS. Our results show that “Renewable Energy Consumption” is positively associated among others to “Cooling Degree Days” and “Adjusted savings: net forest depletion” and negatively associated among others to “GHG net emissions/removals by LUCF” and “Mean Drought Index”. Furthermore, we perform a cluster analysis with the application of the k-Means algorithm optimized with the Silhouette Coefficient and we find the presence of two clusters. Finally, we compare eight different machine learning algorithms to predict the value of Renewable Energy Consumption. Our results show that the Polynomial Regression is the best algorithm in the sense of prediction and that on average the renewable energy consumption is expected to growth of 2.61%.

In this article we investigate the impact of “Renewable Electricity Output” on green economy in the context of circular economy for 193 countries in the period 2011-2020. We use data from World Bank ESG framework. We perform Panel Data with Fixed Effects, Panel Data with Random Effects, WLS, and Pooled OLS. Our results show that Renewable Electricity Output is positively associated, among others, to “Adjusted Savings-Net Forest Depletion” and “Renewable Energy Consumption” and negatively associated, among others, to “CO2 Emission” and “Cooling Degree Days”. Furthermore, we perform a cluster analysis implementing the k-Means algorithm optimized with the Elbow Method and we find the presence of 4 clusters. Finally, we confront seven different machine learning algorithms to predict the future level of “Renewable Electricity Output”. Our results show that Linear Regression is the best algorithm and that the future value of renewable electricity output is predicted to growth on average at a rate of 0.83% for the selected countries.

We investigate the innovational determinants of “Patent Applications” in Europe. We use data from the European Innovation Scoreboard-EIS of the European Commission for 36 countries in the period 2010-2019. We use Panel Data with Fixed Effects, Panel Data with Random Effects, Pooled OLS, WLS and Dynamic Panel. We found that the variables that have a deeper positive association with “Patent Applications” are “Human Resources” and “Intellectual Assets”, while the variables that show a more intense negative relation with Patent Applications are “Employment Share in Manufacturing” and “Total Entrepreneurial Activity”. A cluster analysis with the k-Means algorithm optimized with the Silhouette Coefficient has been realized. The results show the presence of two clusters. A network analysis with the distance of Manhattan has been performed and we find three different complex network structures. Finally, a comparison is made among eight machine learning algorithms for the prediction of the future value of the “Patent Applications”. We found that PNN-Probabilistic Neural Network is the best performing algorithm. Using PNN the results show that the mean future value of “Patent Applications” in the estimated countries is expected to decrease of -0.1%.

In this article we have estimated the value of “Opportunity Driven Entrepreneurship” in Europe. We use data from European Innovation Scoreboard-EIS of the European Commission for 36 countries in the period 2010-2019. We use Panel Data with Fixed Effects, Panel Data with Random Effects, WLS, Pooled OLS, and Dynamic Panel. Our results show that “Opportunity Driven Entrepreneurship” is positively associated, among others, to “Innovation Friendly Environment” and “Turnover Share Large Enterprises”, while it is negatively associated, among others, to “Sales Impacts” and “R&D Expenditure Business Sectors”.

In this article we estimate the value of “Non-R&D Innovation Expenditures” in Europe. We use data from the European Innovation Scoreboard-EIS of the European Commission from the period 2010-2019. We test data with the following econometric models i.e.: Pooled OLS, Dynamic Panel, Panel Data with Fixed Effects, Panel Data with Random Effects, WLS. We found that “Non-R&D Innovation Expenditures” is positively associated among others to “Innovation Index” and “Firm Investments” and negatively associated among others to “Human Resources” and “Government Procurement of Advanced Technology Products”. We use the k-Means algorithm with either the Silhouette Coefficient and the Elbow Method in a confrontation with the network analysis optimized with the Distance of Manhattan and we find that the optimal number of clusters is four. Furthermore, we propose a confrontation among eight machine learning algorithms to predict the level of “Non-R&D Innovation Expenditures” either with Original Data-OD either with Augmented Data-AD. We found that Gradient Boost Trees Regression is the best predictor for OD while Tree Ensemble Regression is the best Predictor for AD. Finally, we verify that the prediction with AD is more efficient of that with OD with a reduction in the average value of statistical errors equal to 40,50%.

In this article we investigate the determinants of “New Doctorate Graduates” in Europe. We use data from the EIS-European Innovation Scoreboard of the European Commission for 36 countries in the period 2010-2019 with Pooled OLS, Dynamic Panel, WLS, Panel Data with Fixed Effects and Panel Data with Random Effects. We found that “New Doctorate Graduates” is positively associated, among others, with “Human Resources” and “Government Procurement of Advanced Technology Products” and negatively, associated among others, with “Total Entrepreneurial Activity” and “Innovation Index”. We apply a clusterization with k-Means algorithm either with the Silhouette Coefficient either with the Elbow Method and we found that in both cases the optimal number of clusters is three. Furthermore, we use the Network Analysis with the Distance of Manhattan, and we find the presence of seven network structures. Finally, we propose a confrontation among ten machine learning algorithms to predict the value of “New Doctorate Graduates” either with Original Data-OD either with Augmented Data-AD. Results show that SGD-Stochastic Gradient Descendent is the best predictor for OD while Linear Regression performs better for AD.

In this article we investigate the determinants of the European “Most Cited Publications”. We use data from the European Innovation Scoreboard-EIS of the European Commission for the period 2010-2019. Data are analyzed with Panel Data with Fixed Effects, Panel Data with Random Effects, WLS, and Pooled OLS. Results show that the level of “Most Cited Publications” is positively associated, among others, to “Innovation Index” and “Enterprise Birth” and negatively associated, among others, to “Government Procurement of Advanced Technology Products” and “Human Resources”. Furthermore, we perform a cluster analysis with the k-Means algorithm either with the Silhouette Coefficient and the Elbow Method. We find that the Elbow Method shows better results than the Silhouette Coefficient with a number of clusters equal to 3. In adjunct we perform a network analysis with the Manhattan distance, and we find the presence of 4 complex and 2 simplified network structures. Finally, we present a confrontation among 10 machine learning algorithms to predict the level of “Most Cited Publication” either with Original Data-OD either with Augmented Data-AD. Results show that the best machine learning algorithm to predict the level of “Most Cited Publication” with Original Data-OD is SGD, while Linear Regression is the best machine learning algorithm for the prediction of “Most Cited Publications” with Augmented Data-AD.

In this article we analyze the determinants and the export trend of European countries of medium and high technology products. The data were analyzed using various econometric models, namely WLS, Pooled OLS, Dynamic Panel, Panel Data with Fixed Effects, Panel Data with Random Effects. The results show that exports of medium and high-tech products are positively associated, among other variables, with the value of “Average Annual GDP Growth”, “Total Entrepreneurial Activity” and “Sales Impacts”, and negatively associated with, among other variables, “Human Resources”, “Government and Procurement of Advanced Technology Products” and “Buyer Sophistication”. A cluster analysis was realized with the k-Means algorithm optimized with the Silhouette coefficient. The result showed the presence of only two clusters. Since this result was considered poorly representative of the industrial complexity of the European Union countries, a further analysis was carried out with the Elbow method. The result showed the presence of 6 clusters with the dominance of Germany and the economies connected to the German economy. In addition, a network analysis was carried out using the distance to Manhattan. Four complex network structures and two simplified network structures were detected. A comparison was then made between 10 machine learning algorithms for predicting the value of exports of medium and high-tech products. The result shows that the best performing algorithm is the SGD. An analysis with Augmented Data-AD was implemented with a comparison between 10 machine learning algorithms for prediction and the result shows that the Linear Regression algorithm is the best predictor. The prediction with the Augmented Data-AD allows to reduce the MAE by about 0.0022131 compared to the prediction with the Original Data-OD.

The article affords the question of lifelong learning in Europe using data from the European Innovation Scoreboard-EIS in the period 2010-2019 for 36 countries. The econometric analysis is realized using WLS, Dynamic Panel, Pooled OLS, Panel Data with Fixed Effects and Random Effects. The results show that lifelong learning is, among other variables, positively associated to “Human Resources” and “Government procurement of advanced technology products” and is negatively associated, among others, to “Average annual GDP growth” and “Innovation Index”. A clusterization is realized using the k-Means algorithm with a confrontation between the Elbow Method and the Silhouette Coefficient. Subsequently, a Network Analysis was applied with the distance of Manhattan. The results show the presence of 4 complex and 2 simplified network structures. Finally, a comparison was made among eight machine learning algorithms for the prediction of the value of lifelong learning. The results show that the linear regression is the best predictor algorithm and that the level of lifelong learning is expected to growth on average by 1.12%.

In this article we investigate the determinants of marketing or organizational innovators in Europe for 36 countries in the period 2010-2019. We have used data from the European Innovation Scoreboard-EIS of the European Commission. We perform different econometric models i.e. Dynamic Panel, Pooled OLS, Panel Data with Fixed Effects, Panel Data with Random Effects, WLS. Results show that the level of marketing or organizational innovators in positively associated, among others variables to “Innovation Index”, “Innovators” and “Knowledge Intensive Service Exports”, while is negatively associated with “Sales Impacts”, “Foreign Controlled Enterprises Share of Value Added” and “Government procurement of advanced technology products”.

In the following article, the value of the "Knowledge Intensive Services Exports in Europe" in 36 European countries is estimated. The data were analyzed through a set of econometric models or: Poled OLS, Dynamic Panel, Panel Data with Fixed Effects, Panel Data with Random Effects, WLS. The results show that “Knowledge Intensive Services Exports” is negatively associated, among others, with "Buyer Sophistication", "Government Procurement of Advanced Technology Products", and positively associated with the following variables i.e. "Innovation Index", "Sales Impacts" and "Total Entrepreneurial Activity". Then a clusterization with k-Means algorithm was made with the Elbow method. The results show the presence of 3 clusters. A network analysis was later built and 4 complex network structures and three structures with simplified networks were detected. To predict the future trend of the variable, a comparison was made with eight different machine learning algorithms. The results show that prediction with Augmented Data-AD is more efficient than prediction with Original Data-AD with a reduction of the mean of statistical errors equal to 55,94%.

The following article analyzes the determinants of the innovation index in Europe. The data refer to the European Innovation Scoreboard-EIS of the European Commission for the period between 2010 and 2019 for 36 countries. The data are analyzed using the following econometric techniques: Panel Data with Random Effects, Panel Data with Fixed Effects, Dynamic Panel Data, Pooled OLS, WLS. The results show that the Innovation Index is negatively connected to some variables, among which the most significant are "GDP per capita", "R&D expenditure public sector", "Venture capital", "Tertiary education", and positively connected to some variables among which the most relevant are: "Government procurement of advanced technology products", "Average annual population growth", "Finance and support", "Human resources", "Marketing or organisational innovators", "Linkages". A clustering was then carried out using the unsupervised k-Means algorithm optimized with the Silhouette coefficient which shows the presence of 2 clusters per value of the Innovation Index. Eight machine learning algorithms has been used for prediction with real data. The Tree Ensemble Regression algorithm has been chosen as best performer. A further prediction has been made with the augmented data. The result shows that the best performing algorithm is Linear Regression with an innovation index value predicted to grow by approximately 3.38%.

The following article analyzes the determinants of e-government in 28 European countries between 2016 and 2021. The DESI-Digital Economy and Society Index database was used. The econometric analysis involved the use of the Panel Data with Fixed Effects and Panel Data with Variable Effects methods. The results show that the value of “e-Government” is negatively associated with “Fast BB (NGA) coverage”, “Female ICT specialists”, “e-Invoices”, “Big data” and positively associated with “Open Data”, “e-Government Users”, “ICT for environmental sustainability”, “Artificial intelligence”, “Cloud”, “SMEs with at least a basic level of digital intensity”, “ICT Specialists”, “At least 1 Gbps take-up”, “At least 100 Mbps fixed BB take-up”, “Fixed Very High Capacity Network (VHCN) coverage”. A cluster analysis was carried out below using the unsupervised k-Means algorithm optimized with the Silhouette coefficient with the identification of 4 clusters. Finally, a comparison was made between eight different machine learning algorithms using "augmented data". The most efficient algorithm in predicting the value of e-government both in the historical series and with augmented data is the ANN-Artificial Neural Network.

This article analyzes the determinants of the "Broadband Price Index" in Europe. The data used refer to 28 European countries between 2016 and 2021. The database used is the Digital, Economy and Society Index-DESI of the European Commission. The data were analyzed using the following econometric techniques, namely Panel Data with Random Effects, Panel Data with Fixed Effects, Pooled OLS, WLS and Dynamic Panel. The value of the "Broadband Price Index" is positively associated with the DESI Index, and "Connectivity" while it is negatively associated with "Fixed Broadband Take Up", "Fixed Broadband Coverage", "Mobile Broadband", "e-Government", "Advanced Skills and Development", "Integration of Digital Technology", "At Least Basic Digital Skills ", "Above Basic Digital Skills "," At Least Basic Software Skills ". A cluster analysis was carried out below using the k-Means algorithm optimized with the Silhouette coefficient. The analysis revealed the existence of three clusters. Finally, an analysis of the machine learning algorithms was carried out to predict the future value of the "Broadband Price Index". The result shows that the most useful algorithm for prediction is the Artificial Neural Network-ANN with an estimated value equal to an amount of 9.21%.

We have estimated the level of Risk Weighted Assets among 30 countries in Europe, in 30 trimesters, using data of the European Banking Authority-EBA of 139 variables. We perform an econometric model using Pooled OLS, Panel Data with Fixed Effects, Panel Data with Random Effects, Weighted Least Squares. We found that Risk Weighted Assets is negatively associated, among others, to the level of NFC loans in mining and quarrying, in public administration and defence, and in financial and insurance activities and positively associated, among others to distribution of NFC loans in human health services and social work activities, in education and the level of net fee and commission income. Furthermore, we apply a cluster analysis with the k-Means algorithm, and we find the presence of two clusters. A comparison was then made between eight different machine learning algorithms for predicting the value of the RWAs and we found that the best predictor is the linear regression. The RWA value is predicted to increase by 1.5%.

The determinants of the presence of “Foreign Doctorate Students” among 36 European Countries for the period 2010-2019 are analyzed in this article. Panel Data with Fixed Effects, Random Effects, WLS, Pooled OLS, and Dynamic Panel are used to investigate the data. We found that the presence of Foreign Doctorate Students is positively associated to “Attractive Research Systems”, “Finance and Support”, “Rule of Law”, “Sales Impacts”, “New Doctorate Graduates”, “Basic School Entrepreneurial Education and Training”, “Tertiary Education” and negatively associated to “Innovative Sales Share”, “Innovation Friendly Environment”, “Linkages”, “Trademark Applications”, “Government Procurement of Advanced Technology Products”, “R&D Expenditure Public Sectors”. A cluster analysis was then carried out through the application of the unsupervised k-Means algorithm optimized using the Silhouette coefficient with the identification of 5 clusters. Finally, eight different machine learning algorithms were used to predict the value of the "Foreign Doctorate Students" variable. The results show that the best predictor algorithm is the "Tree Ensemble Regression" with a predicted value growing at a rate of 114.03%.

The determinants of enterprises providing ICT training in Europe are analyzed in this article. Data are collected from the European Innovation Scoreboard-EIS of the European Commission for 36 European countries in the period 2000-2019. Data are analyzed with Panel Data with Fixed Effects, Panel Data with Random Effects, Dynamic Panel, WLS and Pooled OLS. Results show that the number of enterprises providing ICT training in Europe is positively associate with “Innovation Index”, “Innovators”, “New Doctorate Graduates”, “Tertiary Education” and negatively associated with “Government Procurement of Advanced Technology Products”, “Human Resources”, and “Marketing or Organisational Innovators”. In adjunct a cluster analysis is performed by using k-Means algorithm optimized with the Silhouette Coefficient and we find the presence of four clusters. Finally, we use eight different machine learning algorithms to predict the value of the enterprises providing ICT training in Europe. We found that the Simple Tree Regression is the best predictor and that the number of enterprises providing ICT training in Europe is expected to growth of the 5,02%.

This article presents an estimation of the hypertension risk based on a dataset on 1007 individuals. The application of a Tobit Model shows that “Hypertension” is positively associated to “Age”, “BMI-Body Mass Index”, and “Heart Rate”. The data show that the element that has the greatest impact in determining inflation risk is “BMI-Body Mass Index”. An analysis was then carried out using the fuzzy c-Means algorithm optimized with the use of the Silhouette coefficient. The result shows that the optimal number of clusters is 9. A comparison was then made between eight different machine-learning algorithms for predicting the value of the Hypertension Risk. The best performing algorithm is the Gradient Boosted Trees Regression according to the analyzed dataset. The results show that there are 37 individuals who have a predicted hypertension value greater than 0.75, 35 individuals who have a predicted hypertension value between 0.5 and 0.75, while 227 individuals have a hypertension value between 0.0 and 0.5 units.

The following article indicates the determinants of “Internet User Skills” among European countries based on the application of the database deriving from the DESI-Index. The data were analyzed using the following econometric models, namely: Panel Data with Fixed Effects, Panel Data with Random Effects, Pooled OLS, WLS, WLS corrected for heteroskedasticity. The Elbow method and the Silouette coefficient method were compared for the optimization of the number of clusters obtained by the k-Means algorithm. The result shows the presence of 5 clusters. A network analysis was carried out using the Euclidean distance with the result of identifying two network structures between some analyzed countries. subsequently a comparison was made between six different machine learning algorithms for the prediction of the future value of the variable of interest. The result shows that the best predictor algorithm is Gradient Boosted Tree Regression with an expected value of the predicted variable increasing by a value of 1.75%. Later a further comparison was made by comparing 6 algorithms with the increased data. The result shows that the best predictor is Simple Regression Tree. The interest variable is predicted to decrease by an amount equal to -6.099%. Statistical errors improve on average by 32.43% in the transition between the original data and the increased data.

The following article estimates the value of ICT Specialists in Europe between 2016 and 2021 for 28 European countries. The data were analyzed using the following econometric techniques, namely: Panel Data with Fixed Effects, Panel Data with Random Effects, WLS and Pooled OLS. The results show that the value of ICT Specialists in Europe is positively associated with the following variables: "Desi Index", "SMEs with at least a basic level of digital intensity", "At least 100 Mbps fixed BB take-up" and negatively associated with the following variables: "4G Coverage","5G Coverage", "5G Readiness", "Fixed broadband coverage", "e-Government", "At least Basic Digital Skills", "Fixed broadband take-up", "Broadband price index", "Integration of Digital Technology". Subsequently, two European clusters were found by value of "ICTG Specialists" using the k-Means clustering algorithm optimized by using the Silhouette coefficient. Finally, eight different machine learning algorithms were compared to predict the value of "ICT Specialists" in Europe. The results show that the best prediction algorithm is ANN-Artificial Neural Network with an estimated growth value of 12.53%. Finally, "augmented data" were obtained through the use of the ANN-Artificial Neural Network, through which a new prediction was made which estimated a growing value of the estimated variable equal to 3.18%.

The following article presents an analysis of the determinants of diabetes using a dataset containing the surveys of 2000 patients from the Frankfurt Hospital in Germany. The data were analyzed using the following models, namely: Tobit, Probit, Logit, Multinomial Logit, OLS, WLS with heteroskedasticity. The results show that the presence of diabetes is positively associated with "Pregnancies", "Glucose", "BMI", "Diabetes Pedigree Function", "Age" and negatively associated with "Blood Pressure". A cluster analysis is realized using the fuzzy c-Means algorithm optimized with the Elbow method and three clusters were found. Finally a confrontation among eight different machine learning algorithms is realized to select the best performing algorithm to predict the probability of patients to develop diabetes.