Project overview

What KERI is building.

The project explores whether combinations of metabolic, clinical, and behavioral variables can reveal early warning patterns linked to cancer among people with diabetes.

Research aim

Identify whether multi-variable metabolic patterns can support earlier risk stratification for cancer in diabetes contexts.

Method stack

The current workflow combines causal reasoning, a predictive baseline, and data preparation around the NHANES merged dataset.

Clinical value

The project is framed around earlier identification of higher-risk individuals so the signal can be reviewed before symptoms become advanced.

Variables of interest

Signals to examine in the data

Diabetes timing

Type, onset, and duration of diabetes.

Metabolic markers

HbA1c, insulin, C-peptide, and related biomarkers.

Patient profile

Age, sex, obesity, weight change, and broader clinical context.

Open Source Development

Code and model development

Code repository

The project is open source and available on GitHub. The repository includes scripts for data preparation, model training, and evaluation.

View on GitHub

Model development

The model is being developed in Python using libraries such as scikit-learn, pandas, and NumPy. The workflow includes data cleaning, feature engineering, and model evaluation.

The project aims to provide a baseline predictive model that can accurately identify individuals at higher risk of cancer based on their metabolic and clinical profiles.