Enhancing the OhioT1DM Dataset for Predictive Modeling: Exploratory Analysis, Feature Engineering, and Correlation Insights

Abstract

Type 1 diabetes mellitus is a serious illness, and the lack of open-source datasets for a long time has made it difficult to perform research on continuous glucose monitoring and blood glucose level prediction. The OhioT1DM dataset presents an excellent resource upon which to perform research on glucose variability and develop predictive models. In this study, we perform a thorough exploratory data analysis of the OhioT1DM dataset, focusing on data quality assessment, time-series stationarity, and data enhancement (i.e., feature augmentation/feature engineering). We begin by examining missing values and the time differences between consecutive timestamps to understand gaps and inconsistencies in the data. Stationarity is checked for each patient’s glucose levels to ascertain the constancy of the time-series data, which is crucial for accurate forecasting. To enrich the dataset, we introduce new features that capture important physiological and behavioral trends, such as insulin dose variability and daily exercise participation. We also conduct correlation analysis of all features against glucose levels. The findings indicate that the fingerstick measurements exhibit the highest positive correlation to glucose, followed by basal and temporary basal insulin, while exercise intensity shows a strong negative correlation. Although correlation analysis provides useful preliminary insights, it is inherently limited in capturing nonlinear or lagged dependencies; thus, future research should extend this work with nonlinear and time-aware analytical methods. This study contributes to overcoming key data challenges in the OhioT1DM dataset while introducing innovative features that can improve the performance of machine learning models for glucose prediction. The results support ongoing progress toward more accurate and reliable diabetes management solutions.

Keywords

• Continuous Glucose Monitoring
• Blood Glucose Level Prediction
• Exploratory Data Analysis
• OhioT1DM Dataset
• Time-Series Stationarity

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