AN INTELLIGENT COMPUTATIONAL APPROACHES FOR DIABETES RISK PREDICTION: A PROACTIVE HEALTHCARE PARADIGM DIABOLIC
DOI:
https://doi.org/10.29121/shodhkosh.v5.i7.2024.1899Keywords:
Diabetes Detection, Internet of Things (IoT), Artificial Intelligence (AI), Tumultuo Dwarf Mongoose Optimization (TuD-MO) Technique, Diabetic Prediction Utilizing Optimized Learning Classifier (DIABOLIC), and Fused Deep Convolution Random Network (FDCRN)Abstract [English]
Diabetes mellitus, a long-term metabolic disease marked by high blood glucose levels, is a major global health concern. Diabetes must be identified and treated early to reduce complications and enhance patient outcomes. In this study, we propose a unique deep learning framework, named as, Diabetic prediction utilizing Optimized Learning Classifier (DIABOLIC) for diabetes detection.The original contribution of this paper is to develop a resilient prediction model by leveraging an advanced computational algorithms to reliably predict the probability of getting diabetes. In the proposed framework, a special Tumultuo Dwarf Mongoose Optimization (TuD-MO) technique is used to extract the most important and critical features from the preprocessed dataset. Also, a Fused Deep Convolution Random Network (FDCRN) is developed to precisely identify diabetic patients based on the selected attributes. Moreover, a detailed performance analysis is completed in order to validate and extensively explore the outcomes of the DIABOLIC model.Our test findings show that, when it comes to diabetes detection, DIABOLIC outperforms cutting-edge techniques in terms of predictive performance, with excellent sensitivity, specificity, and accuracy. In addition, we perform thorough interpretability investigations in order to clarify the underlying characteristics and processes that underlie the predictions produced by DIABOLIC. Overall, our research shows how deep learning techniques, like DIABOLIC, can improve diabetes detection and tailored healthcare plans, which will benefit public health campaigns and patient outcomes.
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