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. 2025 Jan 18;25(1):30.
doi: 10.1186/s12872-024-04374-0.

Early Prediction of Cardio Vascular Disease (CVD) from Diabetic Retinopathy using improvised deep Belief Network (I-DBN) with Optimum feature selection technique

T K Revathi  1 , B Sathiyabhama  1 , S Kaliraj  2 , Vidhushavarshini Sureshkumar  3
Affiliations

Early Prediction of Cardio Vascular Disease (CVD) from Diabetic Retinopathy using improvised deep Belief Network (I-DBN) with Optimum feature selection technique

T K Revathi et al. BMC Cardiovasc Disord. .

Abstract

Cardio Vascular Disease (CVD) is one of the leading causes of mortality and it is estimated that 1 in 4 deaths happens due to it. The disease prevalence rate becomes higher since there is an inadequate system/model for predicting CVD at an earliest. Diabetic Retinopathy (DR) is a kind of eye disease was associated with increasing risk factors for all-causes of CVD events. The early diagnosis of DR plays a significant role in preventing CVD. However, there are many works have been carried out on classification of the disease but they focused less on feature selection and increasing the accuracy of the model. The proposed work introduces Improvised Deep Belief Network named I-DBN to resolve the above mentioned problems and mainly to concentrate on improving the entire performance of the model leading to the unbiased output. We used Principal Component Analysis (PCA) and Particle Swarm Optimization (PSO) algorithm for feature extraction and selection respectively. Five performance metrics have been used to assess the proposed model. The results of I-DBN outperform other state-of-the-art methods. The result validation ensures that I-DBN can deliver trustworthy recommendations to doctors to treat the patients by enhancing the accuracy of CVD prediction up to 98.95%.

Keywords: Cardiovascular disease; Deep belief network; Diabetic retinopathy; Prediction.

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Conflict of interest statement

Declarations. Ethics approval and consent to participate: Not applicable. This study did not involve any human participants, animals, or data requiring ethical approval or consent to participate. Consent for publication: Not applicable. This study does not involve any individual person’s data in any form (including individual details, images, or videos) that would require consent for publication. Competing interests: The authors declare no competing interests.

Figures

Fig. 1
Fig. 1
The Proposed I-DBN Model
Fig. 2
Fig. 2
Data Projection in subspace
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Algorithm 1: PCA
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Algorithm 2: PSO
Fig. 3
Fig. 3
Schematic diagram of RBMs
Fig. 4
Fig. 4
Schematic diagram of DBN with three hidden layers
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Algorithm 3: Block-Gibbs MCMC Algorithm
Fig. 5
Fig. 5
Performance Evaluation of I-DBN with other classifiers on accuracy
Fig. 6
Fig. 6
Performance Evaluation of I-DBN with other classifiers on precision
Fig. 7
Fig. 7
Performance Evaluation of I-DBN with other classifiers on recall
Fig. 8
Fig. 8
Performance Evaluation of I-DBN with other classifiers on sensitivity
Fig. 9
Fig. 9
Performance Evaluation of DBN with other classifiers on specificity
See this image and copyright information in PMC

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