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<p>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.</p>

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An efficient model for diabetic detection using heuristic approach based serial cascaded convolutional ensemble network

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年8月13日

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  • Santosh Kumar Bejugam,
  • Jyothi Vankara

摘要

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.