Key result
The proposed Serial Cascaded Convolutional Ensemble Network (SCCEN) with FBIO achieved high detection accuracies of 97.4%, 97.31%, and 96.69% across three diabetes datasets, outperforming conventional techniques.
Why the study?
Existing diabetes detection models face issues regarding security and privacy of private medical information, as well as unclear characteristics causing difficulty in healthcare facilities.
The introduced SCCEN framework with FBIO optimization achieved high accuracy (>96%) in detecting diabetes across multiple datasets.
Privacy vulnerabilities in AI diabetes detectors caution against clinical deployment; leaves open development of secure, validated models.
Diabetes is a chronic pathology that poses significant risks to people. If diabetes is not properly diagnosed and treated, it may contribute to serious health problems. Delayed diagnosis causes many health issues and leads to numerous deaths every year. So, researchers have developed efficient diabetes detection systems for the early detection of this pathology. However, the existing model raises serious issues about the security and privacy of private medical information, and it requires rigorous safety precautions to prevent intrusions and unapproved access. In addition, the unclear characteristics of existing models cause difficulty in healthcare facilities. Thus, the advanced deep learning-based diabetic detection model was designed in this work to overcome these challenges. Also, it aims to detect diabetics and helps to prevent the progression of diabetes in patients. At first, the required data is gathered from the online data source and then fed to the optimal feature selection phase. Here, the features and weight are optimally selected using the Fitness-based Billiards-Inspired Optimization (FBIO) algorithm. This process helps the model to focus on the most impactful information within the data. Further, the obtained optimal weighted feature is passed to the Serial Cascaded Convolutional Ensemble Network (SCCEN) for detection. Here, the SCCEN model serially cascades techniques such as Convolutional Autoencoder (CAE), “1-dimensional Convolutional Neural Network” (1DCNN), and “Convolutional Long Short-Term Memory” (ConvLSTM). This process helps to improve the detection accuracy. Finally, the designed approach’s effectiveness is analyzed by comparing its performance with existing techniques. The suggested approach’s accuracy for dataset-1 is 97.4%, dataset-2 is 97.31%, and dataset-3 is 96.69%, which is higher than the conventional techniques and optimization algorithms. Thus, the result proved that the introduced framework can detect diabetics in premature stages and help the patient to take suitable treatment.
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Santosh Kumar Bejugam (2025) studied Diabetes (n=255,216). Serial Cascaded Convolutional Ensemble Network (SCCEN) with FBIO vs. Conventional machine learning and optimization techniques was evaluated on Detection accuracy. The proposed Serial Cascaded Convolutional Ensemble Network (SCCEN) with FBIO achieved high detection accuracies of 97.4%, 97.31%, and 96.69% across three diabetes datasets, outperforming conventional techniques.
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