Many students are faced with the challenge of deciding on a suitable career path. This is because decisions are characterized by a number of subjective judgements. Therefore, choosing a particular career path without first determining the suitability of a student, as a fundamental step, will yield an undesirable outcome. This paper aims at developing a career path decision making model for senior secondary schools. The concept of fuzzy logic was used in developing the model. Crisp sets are converted to fuzzy sets using fuzzy K- nearest neighbour algorithm method. The model was implemented in the MATLAB environment. The performance of the model was evaluated using specificity and accuracy as performance metrics. The results obtained showed the model has accuracy value of 90.22%. This result show that the model is approximately 90% accurate. Also, it has a specificity value of 96.97%. These results show that the model provides a good support for decision making while eliminating the challenges of indecision and floundering that are characterized with choosing a career path among upper basic school students, that is, Junior Secondary School students. The model will also serve as a tool in enhancing the work of career experts.

Diabetes mellitus is an incurable disease with global prevalence and exponentially increasing incidence. It is one of the greatest health hazards of the twenty-first century which poses a great economic threat on many nations. The premise behind effective disease management in healthcare system is to ensure coordinated intervention targeted towards reducing the incidence of such disease. This paper presents an approach to reducing the incidence of diabetes by predicting the risk of diabetes in patients. Diabetes mellitus risk prediction model was developed using supervised machine learning algorithms of Naïve Bayes, Support Vector Machine and J48 Decision Tree. The decision tree was able to give a prediction accuracy of 95.09% using rules of prediction that give acceptable results, that is, the model was approximately 95% accurate.  The easy-to-understand rules of prediction got from J48 decision tree make it excellent in developing predictive models.