Predicting employability in an unstable developing country requires the use of contextual factors as predictors and a suitable machine learning model capable of generalization. This study has discovered that parental financial stability, sociopolitical, relationship, academic, and strategic factors are the factors that can contextually predict the employability of information technology (IT) graduates in the democratic republic of Congo (DRC). A deep stacking predictive model was constructed using five different multilayer perceptron (MLP) sub models. The deep stacking model measured good performance (80% accuracy, 0.81 precision, 0.80 recall, 0.77 f1-score). All the individual models could not reach these performances with all the evaluation metrics used. Therefore, deep stacking was revealed to be the most suitable method for building a generalizable model to predict employability of IT graduates in the DRC. The authors estimate that the discovery of these contextual factors that predict IT graduates’ employability will help the DRC and other similar governments to develop strategies that mitigate unemployment, an important milestone to achievement of target 8.6 of the sustainable development goals.

Envisaging an adequate IT/IS solution that can mitigate the employability problems is imperative because nowadays there is a high rate of unemployed graduates. Thus, the main goal of this systematic literature review (SLR) was to explore the application of data mining techniques in modeling employability and see how those techniques have been applied and which factors/variables have been retained to be the most predictors or/and prescribers of employability. Data mining techniques have shown the ability to serve as decision support tools in predicting and even prescribing employability. The review determined and analyzed the machine learning algorithms used in data mining to either predict or prescribe employability. This review used the PRISMA method to determine which studies from the existing literature to include as items for this SLR. Hence, 20 relevant studies, 16 of which are predicting employability and 4 of which are prescribing employability. These studies were selected from reliable databases: ScienceDirect, Springer, Wiley, IEEE Xplore, and Taylor and Francis. According to the results of this study, various data mining techniques can be used to predict and/or to prescribe employability. Furthermore, the variables/factors that predict and prescribe employability vary by country and the type of prediction or prescription conducted research.  Nevertheless, all previous studies have relied more on skill as the main factor that predict and/or prescribe employability in developed countries and none studies have been conducted in unstable developing countries. Therefore, the need to conduct research on predicting or prescribing employability in such countries by trying to use contextual factors beyond skill as features.

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In this research, the authors found that statistical analysis is very important preliminary phase in Machine Learning, especially for regression problems. Indeed, when the authors developed the first single models using the same algorithms and the same dataset, they obtained poor performances. After verifying the assumptions of the multiple linear regression, they adjusted the used data and produced efficient models. Moreover, as the objective was to apply the stacking model to predict Patient's Length of Stay in a semi urban hospital, the results showed that the stacking regressor performed better than the seven different models implemented (Random Forest, Extra Trees, Decision Tree, XGBoost, Multilayer perceptron, Light GBM, Support Vector Regressor (SVR)) taken individually. The authors combined Random Forest Regressor, Extra Trees Regressor, Decision Tree Regressor, XGBoost, Light GBM, and SVR to build the stacking model. Using secondary data from four services (Pediatrics, Hospitalization, Gynecology, and Neonatology) of a semi-urban hospital, located in a region of ongoing war in eastern Democratic Republic of Congo (DRC), the study examined the minimum length of stay of a patient in hospital when admitted in one of the four above services. Performances were evaluated using MAE, RMSE, MSE, R-squared and Accuracy. The stacking regression model shifted from 85% of accuracy before statistical analysis phase to 91% after applying statistics and from 0.75 to 0.91 as R-squared

The purpose of this research was to identify factors that affect the resistance of the population of eastern DR Congo to be vaccinated against Covid-19. Therefore, quantitative research based on primary data obtained from a survey was conducted in North Kivu Province. The questionnaire had 35 items with 410 respondents. After processing of the data, 318 records were retained. There were 151 women (48.55%) and 160 men (51.45%). Using the exploratory factor analysis (EFA), the authors shifted from 35 items to 6 main factors affecting vaccine resistance when they used Cronbach's Alpha test for each rotated factor. Cronbach's Alpha test for the reliability of the questionnaire was 0.67; the Bartlett test was 2490.79, and the KMO test was 0.70. Total cumulative variance was 30.1% explained by the six retained which are Psycho-medical background, Demographic, Social origin, Lack of trust in government actions, Previous issues about the Ebola vaccine in North Kivu, and Socio-political belonging.