| Category | Dissertation | Subject | Computer Science |
|---|---|---|---|
| University | Leeds Beckett University (LBU) | Module Title | COMP763 MSc Dissertation |
In this paper, focus is directed toward the use of machine learning classification techniques to predict the prevalence of Diabetes Mellitus in the UK. Since the world is increasingly concerned about the rise of diabetes as a global health challenge, the current work emphasizes how advanced predictive models are important for addressing this health challenge. This study investigates the connection between behavioral risk factors such as smoking, blood pressure, and body mass index (BMI) with diabetes outcomes.
The data set will be systematically collected, preprocessed, and transformed in order to produce quality results using the Knowledge Discovery in Databases methodology. The project compares machine learning models such as logistic regression, decision trees, K-nearest neighbors (KNN), and support vector machines (SVM) to obtain accuracy. It will enhance predictive accuracy while contributing to personalized healthcare approaches by integrating modifiable behavioural risk factors into predictive models.
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