Comparative analysis of diabetes diagnosis: WE-LSTM networks and WizardLM-powered DiabeTalk chatbot

Diabetes is a chronic metabolic disorder characterized by elevated blood glucose levels due to insufficient insulin production or insulin resistance. It primarily manifests in two forms: Type 1 diabetes, an autoimmune condition typically diagnosed in younger individuals, and Type 2 diabetes, which is more prevalent and often linked to lifestyle factors such as obesity and inactivity. This study evaluates the performance of Long Short-Term Memory networks in diagnosing the two types of diabetes from Italian medical text across four progressively refined pre-processing scenarios. Each scenario incrementally builds on the previous one to enhance text cleaning and data preparation, allowing for a more refined and effective data processing pipeline. In parallel, this study introduces DiabeTalk, a chatbot developed on the WizardLM model, designed to provide specialized advice and support for diabetes diagnosis. While the WE-long short term memory models were fine-tuned with clinical data, DiabeTalk was tested without prior training on clinical diaries, allowing us to evaluate its performance in a real-world context. The results indicate that, despite the lack of domain-specific pre-training, DiabeTalk effectively employs natural language understanding and decision-making algorithms to predict diabetes type and respond to user inquiries. However, the testing revealed limitations in accuracy (77.56% versus 97.80%), with the chatbot achieving a lower performance than the WE-long short term memory model, which was applied to minimally pre-processed raw data. The findings underscore the importance of training large language models on relevant clinical datasets to enhance their response capabilities.