Abstract: Diabetes is a chronic health condition that affects millions of individuals worldwide, posing significant risks to their well-being. Early detection and accurate prediction of diabetes are crucial for effective management and prevention. In recent years, deep learning techniques have emerged as a powerful tool for medical diagnosis, particularly in predicting diabetes. However, the inherent complexity and uncertainties in medical data often challenge the performance and interpretability of these models. This project aims to enhance deep learning techniques for diabetes prediction by incorporating fuzzy logic, a computational framework that can handle uncertainty and imprecision in data.
The proposed approach integrates fuzzy logic with deep learning models to improve prediction accuracy and provide more interpretable results. Fuzzy logic is utilized to handle the vagueness and ambiguity associated with medical data, such as variations in test results, patient symptoms, and risk factors. By combining fuzzy logic with neural networks, the model learns to handle fuzzy inputs effectively, offering more robust predictions. The project focuses on implementing hybrid models that leverage fuzzy inference systems (FIS) and deep neural networks (DNN) to predict the likelihood of diabetes in individuals based on medical data such as blood glucose levels, BMI, age, and family history.

A significant advantage of this approach is the interpretability of the model. Unlike traditional deep learning methods that operate as black-box systems, the hybrid model with fuzzy logic offers transparency in decision-making, allowing healthcare professionals to better understand the reasoning behind the predictions. This can lead to more informed decision-making and improved patient care.

The project involves training and evaluating the hybrid model on a diabetes dataset, comparing its performance with conventional deep learning models and other machine learning algorithms. The results are expected to demonstrate enhanced accuracy, interpretability, and reliability, making the model a valuable tool for early diabetes prediction in clinical settings. Ultimately, this research aims to contribute to the development of intelligent healthcare systems that can improve patient outcomes and reduce the burden of diabetes.

Keywords: Diabetes prediction, deep learning, fuzzy logic, neural networks, fuzzy inference systems, healthcare, interpretability, machine learning, medical data, early detection.

| DOI: 10.17148/IARJSET.2025.12324