Interpretable Machine Learning Approaches for Early Diabetes Diagnosis

Abstract

Interpretable machine learning approaches for early diabetes diagnosis aim to combine the predictive power of advanced algorithms with the transparency needed for clinical application. Traditional diagnostic methods, while reliable, cannot often harness the vast amount of data available in modern healthcare. Recent advancements in machine learning offer enhanced predictive capabilities, yet these models are often perceived as black boxes, limiting their acceptance in clinical practice. By integrating interpretable machine learning techniques such as decision trees, logistic regression, and advanced methods like LIME and SHAP, this study seeks to develop models that not only predict diabetes risk with high accuracy but also provide clear insights into the factors driving these predictions. The study uses diverse datasets, including clinical records and population studies, and involves meticulous data cleaning and preprocessing to ensure robustness. Performance evaluation metrics such as accuracy, sensitivity, specificity, and AUC-ROC are employed to compare models. The findings highlight that interpretable models can achieve comparable performance to their black-box counterparts while offering the added benefit of transparency, thus fostering trust and facilitating more informed decision-making in early diabetes diagnosis.