Explainable Artificial Intelligence for Interpreting and Understanding Diabetes Prediction Models

EasyChair Preprint 13785

Abstract

Artificial intelligence (AI) has made significant advancements in healthcare, particularly in the field of diabetes prediction models. However, the lack of interpretability in these models poses challenges in understanding their decision-making process and potential biases. Explainable Artificial Intelligence (XAI) offers a solution by providing transparency and interpretability to AI systems. This paper explores the concept of XAI and its application in interpreting and understanding diabetes prediction models. It discusses various techniques such as rule-based methods, feature importance analysis, SHAP values, and LIME, which enable healthcare professionals and patients to interpret the models' predictions. Real-world applications and case studies demonstrate the benefits of XAI in healthcare, emphasizing the impact on decision-making, patient trust, and improved outcomes. Ethical considerations and future directions are also addressed, highlighting the need for fairness, avoiding bias, and advancing XAI research. Overall, XAI plays a crucial role in enhancing our understanding of diabetes prediction models, empowering healthcare stakeholders with transparent and explainable AI systems.

Keyphrases: Artificial Intelligence, Ethical Considerations, Trustworthiness, interpretability, transparency

@booklet{EasyChair:13785,
  author    = {Kayode Sheriffdeen and Samon Daniel},
  title     = {Explainable Artificial Intelligence for Interpreting and Understanding Diabetes Prediction Models},
  howpublished = {EasyChair Preprint 13785},
  year      = {EasyChair, 2024}}