Intuitive Feature extraction techniques with IDDN Model for Women’s Diabetic Prediction

Abstract

Diabetes prediction for women, especially around pregnancy, is critical due to the elevated risk of gestational diabetes and its long-term health effects. This study presents an advanced approach utilizing the Interpolative Deep Dense Layer Design (IDDLD) algorithm to enhance prediction accuracy and interpretability for complex, multi-phase datasets related to women's health. The IDDLD algorithm improves traditional deep learning frameworks by integrating interpolative techniques within dense neural networks, addressing the dynamic changes in health status before and after pregnancy. By incorporating new features such as indicators for heart disease (based on elevated glucose and blood pressure), kidney disease (high insulin and skin thickness), eye disease (BMI and diabetes pedigree function), and gastrointestinal diabetes (age and glucose levels), the model captures critical health markers associated with diabetes. Evaluations of the IDDLD model using pre-pregnancy and post-pregnancy health records demonstrated significant improvements in predictive accuracy, with higher precision and recall rates compared to traditional models. The algorithm’s ability to adapt to temporal variations and physiological changes offers enhanced interpretability and actionable insights, contributing to more personalized and effective healthcare strategies. This research highlights the potential of the IDDLD model to advance diabetes prediction methodologies, supporting better health outcomes for women across different life stages.