The issue of identifying the prevalence of sickness that is linked to the population of a nation, state, neighborhood, organization, or school has not been taken into consideration by the majority of prior studies on the prediction of illness among populations. They frequently merely choose any sickness based on assumption, while those that determined the prevalence of the condition before developing their framework utilized survey data or data from web repositories, which removes idiosyncrasies from those data. In order to increase performance, this research suggests an enhanced data analytics framework for the predictive diagnosis of common illnesses affecting university students. In order to do this, exploratory data analysis (EDA) using a multivariate analytic technique was conducted using a high-level model methodology using CRISP-DM stages. When the suggested strategy was evaluated on support vector machines, ensemble gradient boosting, random forest, decision tree, K-neighbors, and linear regression machine learning models, experimental findings revealed that it outperformed current methods. In comparison to other reviewed frameworks that used survey datasets, standardized or online repositories' dataset, the framework with emphasis on the ensemble Gradient Boosting classifier and regression had accuracy of 100% and mean absolute error of 0.18, respectively. It is also steady due to its ability to manage both small and big data sets without impacting the model's performance.  The enhanced results through localized dataset demonstrate the benefit of including local data sources in the process of developing models for the diagnosis and prognosis of prevalent illnesses of any area with people.

The extraction of public opinions from online communication platforms can serve several purposes in corporate institutions, state politics, and governance.  The analysis of these opinions may be useful for both immediate business decision making and professional planning. This analysis is becoming relevant in managing social movements and digital activism by applying computational technology. There is a need to deploy this opinion mining technology to the recent largest digital activism in Nigeria known as the #EndSARS movement. In this work, we proposed the EndSARS live analytics framework which holds a promising solution to social unrest and may serve as a panacea to curbing the menace of vandalism resulting from unresolved protest issues. Using a dataset of 12,357 tweets, we demonstrated that computational technology can be relevant to addressing online protests. The result of the analysis shows the eight basic emotions expressed during the protest and approaches the government may adopt to address future activisms.