Adaptive Neural-Fuzzy Rule Generation with Attention Mechanisms for Anthropometric Prediction

Music City Center 

Accurate decision support in healthcare often requires effective handling of uncertainty inherent in clinical data. Traditional fuzzy systems rely on manually crafted rule bases, which can be inflexible and labor-intensive to design. In this work, we propose an adaptive rule generation framework that integrates neural networks with fuzzy logic to automatically derive fuzzy rules from numerical health indicators and demographic variables. Our method utilizes learnable fuzzy membership functions to transform raw data into fuzzy representations and employs an attention mechanism to dynamically assign weights to the generated rules. The overall architecture consists of a fuzzy encoding layer, an adaptive rule generation module, and a subsequent prediction layer, enabling end-to-end training for regression tasks such as the prediction of Body Mass Index (BMI), weight, and waist circumference. By eliminating the reliance on manually defined rules, the proposed framework enhances model flexibility and interpretability, paving the way for more robust and data-driven decision support systems in healthcare.

fuzzy system

healthcare prediction

attention mechanism 

Section on Statistical Learning and Data Science