Cerebrovascular disease commonly known as stroke is the third leading cause of disability and mortality in the world. In recent years, technological advancements have transformed the way information is acquired and how problems are solved in diverse fields of human endeavors, including the medical and healthcare sectors. Machine Learning (ML) and data driven techniques have gain prominence in problem solving and have been deployed in the prediction of the occurrences of stroke. This work explores the application of supervised machine learning algorithms for the prediction of stroke, emphasizing the critical need for early prediction to enhance preventive measures. A comprehensive comparison of classification (Support Vector Machine and Random Forest) and regression (Logistic Regression) algorithms was conducted, with concerns on binary stroke outcome (likelihood of stroke and no stroke) data utilizing dataset from the International Stroke Trial database. The Synthetic Minority Oversampling Technique (SMOTE) and K-fold cross validation were used to balance and address the class imbalance in the datasets. The subsequent model comparison demonstrated distinct strengths and weaknesses among the three models.  Random Forest (RF) exhibited high accuracy score of 89%, Support Vector Machine (SVM) and Logistic Regression (LR) showed 86% accuracy. LR demonstrated the most balanced predictive performance, achieving high precision for stroke cases and reasonable recall for both classes.

Persistent and quality graduation rates of students are increasingly important indicators of progressive and effective educational institutions. Timely analysis of students’ data to guide instructors in the provision of academic interventions to students who are at risk of performing poorly in their courses or dropout is vital for academic achievement. In addition there is need for performance attributes relationship mining for the generation of comprehensible patterns. However, there is dearth in pieces of knowledge relating to predicting students’ performance from patterns. This therefore paper adopts hierarchical cluster analysis (HCA) to analyze students’ performance dataset for the discovery of optimal number of fail courses clusters and partitioning of the courses into groups, and association rule mining for the extraction of interesting course-status association. Agglomerative HCA with Ward’s linkage method produced the best clustering structure (five clusters) with a coefficient of 92% and silhouette width 0.57. Apriori algorithm with support (0.5%), confidence (80%) and lift (1) thresholds were used in the extraction of rules with student’s status as consequent. Out of the twenty one courses offered by students in the first year, seven courses frequently occur together as failed courses, and their impact on the respective students’ performance status were assessed in the rules. It is conjectured that early intervention by the instructors and management of educational activities on these seven courses will increase the students’ learning outcomes leading to increased graduation rate at minimum course duration, which is the overarching objective of higher educational institutions. As further work, the integration of other machine learning and nature inspired tools for the adaptive learning and optimization of rules respectively would be performed.