Synthetic Diabetes Dataset for Machine Learning Classification (2025)

This dataset contains synthetic data related to diabetes prediction generated for academic and research purposes. It includes variables such as age, gender, hypertension, heart disease, BMI, glucose levels, and other medical features that can be used to train and evaluate machine learning models. The dataset was designed to simulate realistic health profiles and support classification tasks (e.g., predicting diabetes status) while ensuring that no personally identifiable information (PII) is included. This dataset was created as part of an academic research project focused on applying machine learning algorithms—such as Logistic Regression and Support Vector Machine—to identify key factors influencing diabetes risk.