Developing an Android-Based Smart Healthcare System for Enhanced Diabetes Prediction Using Data Mining Techniques

This research paper focuses on the development and evaluation of an Android-based smart healthcare system designed to enhance diabetes diagnosis using data mining techniques. Addressing the limitations of traditional healthcare in resource-constrained environments like Zimbabwe, this study leverages integrated Electronic Health Records (EHRs) from the Zimbabwe Defence Forces clinics. The system employs an ensemble machine learning model, combining Support Vector Machines (SVM) and Naïve Bayes, to provide accurate diabetes prediction. Through a mixed-methods approach and action research, the study evaluated the system's effectiveness and its impact on healthcare delivery. Findings indicate that the ensemble model significantly improves diagnostic accuracy for diabetes, achieving approximately 75% prediction capability. This work contributes a viable mobile health solution that facilitates early diabetes diagnosis, improves patient management, and enhances healthcare accessibility in similar settings, thereby promoting a paradigm shift towards technology-driven healthcare.