Diabetes Risk Prediction Model

Abstract: This dataset is sourced from anonymous health check-up records from a hospital, containing a variety of health indicators from different participants. The dataset includes basic information about the participants (such as gender, age, etc.) as well as a series of health metrics, such as blood pressure, weight, and diabetes-related indicators (e.g., urea nitrogen, blood glucose, insulin levels), along with diagnostic recommendations for each participant. Each row of the data represents a health check record, which includes both measured physiological indicators and diagnostic suggestions.

After preprocessing, the dataset has been organized into a format suitable for diabetes risk prediction using deep learning models. The preprocessing steps include data cleaning (such as removing missing and outlier values), normalization (such as scaling data), and feature selection, making the data more suitable for training and testing machine learning models.

The purpose of this dataset is to predict the risk of diabetes using deep learning techniques, providing support for early screening. This dataset can effectively train models to identify high-risk individuals, offering data support for early intervention and treatment of diabetes. The features contained in the dataset are strongly correlated with diabetes, making it suitable for research in the medical and healthcare fields.