Advanced Predictive Modeling for Diabetic Diagnosis: A Hybrid SVM and Deep Learning Framework

Abstract

Diabetes is a developing global health concern that necessitates the creation of precise and reliable diagnostic technologies. This paper provides an innovative predictive modelling framework that combines the strengths of SVM (Support Vector Machine) and DL (Deep Learning) improve diagnosis of diabetic. The suggested hybrid strategy combines enhanced feature engineering, which uses domain-specific knowledge to generate new features, with dimensionality reduction approaches to develop the input data. Normalizing the data in consistent manner ensures the model's stability and scalability across varied patient datasets.

In this system, the principal classifier is Support Vector Machine, which is well-known for its ability to handle high-dimensional data. To increase its analytical capability, we include a DL (Deep Learning) model that notices complicated, non-linear relationships in the data. The framework also investigates the usage of transfer learning, in which features taken from pre- trained DL (Deep Learning) representations are fed into a Support Vector Machine, allowing the model to benefit from high-level abstractions in the data. The hybrid model is intended for real-time use, making quick and accurate predictions in clinical contexts. To address the important need for explainability in medical diagnostics, we use model-agnostic interpretability approaches like SHAP and LIME to identify the elements that influence the model's predictions. This ensures that healthcare practitioners can trust and comprehend the model's results. The study results show that the hybrid Support Vector Machine-Deep Learning architecture considerably increases the accuracy and reliability of diabetic diagnosis when compared to standard approaches. This approach provides a viable alternative for tailored and successful diabetes management that might be adopted in real-world healthcare systems.