Detecting Diabetes Early and Classifying Risk with Interpretable Deep Learning Models

Computer Science

This study presents a novel approach to early detection and risk classification of diabetes using interpretable deep learning models, specifically the Deep Learning Multi-Layer Neural Network (DLMNN) framework. The DLMNN outperforms existing methodologies in prediction accuracy due to enhanced data preprocessing techniques and the MKMC algorithm for diabetes diagnosis. The Deep Learning-based Risk (DSR) classifier streamlines the risk analysis process, providing a detailed risk assessment of patients and quantifying potential risk levels associated with diabetes. The DSR classifier demonstrates the system's ability to classify individuals based on their likelihood of developing diabetes, supporting healthcare professionals in prioritizing high-risk patients and contributing to personalized healthcare strategies. The proposed interpretable deep learning framework for early diabetes detection shows significant advancements in accuracy and computational efficiency compared to traditional methods. The findings emphasize the importance of integrating interpretability into deep learning models, ensuring that predictions made can be understood and trusted by healthcare practitioners. This study lays the groundwork for further exploration into the application of deep learning technologies in diabetes management and emphasizes the potential for these models to improve patient outcomes through early diagnosis and tailored interventions.

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