Generative AI for diabetologists: a concise tutorial on dataset analysis
Abstract
OBJECTIVES: This paper aims to provide a tutorial for diabetologists and endocrinologists on using generative AI to analyze datasets. It is designed to be accessible to those new to generative AI or without programming experience.
METHODS: The paper presents three examples using a real diabetes dataset. The examples demonstrate binary classification with the 'Group' variable, cross-validation analysis, and NT-proBNP regression.
RESULTS: The binary classification achieved a prediction accuracy of nearly 0.9. However, the NT-proBNP regression was not successful with this dataset. The calculated R-squared values indicate a poor fit between the predicted model and the raw data.
CONCLUSIONS: The unsuccessful NT-proBNP regression may be due to insufficient training data or the need for additional determinants. The dataset may be too small or new metrics may be required to accurately predict NT-proBNP regression values. It is crucial for users to verify the generated codes to ensure that they can achieve their desired objectives.
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