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<p>Diabetes is a long-term illness that results&#xa0;in a variety of chronic body damage, such as kidney failure, heart problems, eye damage, depression, and nerve damage. This disease is caused by several risk factors, the most prevalent of which are age, obesity, hypertension, and insulin resistance. As a result, early detection of these chronic diseases is critical in assisting patients in reversing diabetes and living healthy lives. Moreover, the main motive of this research is to enhance the prediction and classification score of the existing models by improved dimensionality reduction. In this study, a new early diabetic prediction model is developed using a new Three-Layer Classifier model. The proposed model is designed by using the Support Vector Machine (SVM), Random Forest (RF), and Improved Restricted Boltzmann machine (I-RBM). At first, the raw data is pre-processed using a data cleaning method, where the Missing Value handling, Unwanted data removal, and noise and Outliers handling are accomplished. The noisy images are filtered/denoised by median filters. Then, from this pre-processed data, features like improved Independent Component Analysis (i-ICA), information gain, joint mutual information (JMI), conditional mutual information (CMI), Correlation, and Joint entropy are extracted. As a result, the performance of the proposed model can be validated with different existing methods such as IRBM, RF-SVM, RBM, RF, and SVM. The developed system provides better accuracy about 98.7% of disease classification accuracy and it is high when compared with the existing methodologies such as SVM, RF, RBM, RF-SVM, and IRBM. The accuracy of SVM is about 87%, the accuracy of RF is 91%, RBM and RF-SVM models have 95% accuracy, and the IRBM model has 96% accuracy. Moreover, the higher accuracy of the proposed model indicates that the system can able to reduce the high dimensionality data to avoid overfitting and thus it can enhance the perfromance.</p>

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Early prediction of diabetics using three-layer classifier model and improved dimensionality reduction

  • 期刊论文

  • 发表日期：

    2025年3月4日

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  • Keerthana G,
  • Mohanasundaram R

摘要

Diabetes is a long-term illness that results in a variety of chronic body damage, such as kidney failure, heart problems, eye damage, depression, and nerve damage. This disease is caused by several risk factors, the most prevalent of which are age, obesity, hypertension, and insulin resistance. As a result, early detection of these chronic diseases is critical in assisting patients in reversing diabetes and living healthy lives. Moreover, the main motive of this research is to enhance the prediction and classification score of the existing models by improved dimensionality reduction. In this study, a new early diabetic prediction model is developed using a new Three-Layer Classifier model. The proposed model is designed by using the Support Vector Machine (SVM), Random Forest (RF), and Improved Restricted Boltzmann machine (I-RBM). At first, the raw data is pre-processed using a data cleaning method, where the Missing Value handling, Unwanted data removal, and noise and Outliers handling are accomplished. The noisy images are filtered/denoised by median filters. Then, from this pre-processed data, features like improved Independent Component Analysis (i-ICA), information gain, joint mutual information (JMI), conditional mutual information (CMI), Correlation, and Joint entropy are extracted. As a result, the performance of the proposed model can be validated with different existing methods such as IRBM, RF-SVM, RBM, RF, and SVM. The developed system provides better accuracy about 98.7% of disease classification accuracy and it is high when compared with the existing methodologies such as SVM, RF, RBM, RF-SVM, and IRBM. The accuracy of SVM is about 87%, the accuracy of RF is 91%, RBM and RF-SVM models have 95% accuracy, and the IRBM model has 96% accuracy. Moreover, the higher accuracy of the proposed model indicates that the system can able to reduce the high dimensionality data to avoid overfitting and thus it can enhance the perfromance.