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. 2025 Apr 4;15(1):11633.
doi: 10.1038/s41598-025-96541-2.

An effective PO-RSNN and FZCIS based diabetes prediction and stroke analysis in the metaverse environment

M Karpagam  1 , S Sarumathi  2 , A Maheshwari  3 , K Vijayalakshmi  3 , K Jagadeesh  4 , V Bereznychenko  5 , R Narayanamoorthi  6
Affiliations

An effective PO-RSNN and FZCIS based diabetes prediction and stroke analysis in the metaverse environment

M Karpagam et al. Sci Rep. .

Abstract

Chronic disease (CD) like diabetes and stroke impacts global healthcare extensively, and continuous monitoring and early detection are necessary for effective management. The Metaverse Environment (ME) has gained attention in the digital healthcare environment; yet, it lacks adequate support for disabled individuals, including deaf and dumb people, and also faces challenges in security, generalizability, and feature selection. To overcome these limitations, a novel probabilistic-centric optimized recurrent sechelliott neural network (PO-RSNN)-based diabetes prediction (DP) and Fuzzy Z-log-clipping inference system (FZCIS)-based severity level estimation in ME is carried out. The proposed system integrates Montwisted-Jaco curve cryptography (MJCC) for secured data transmission, Aransign-principal component analysis (A-PCA) for feature dimensionality reduction, and synthetic minority oversampling technique (SMOTE) to address data imbalance. The diagnosed results are securely stored in the BlockChain (BC) for enhanced privacy and traceability. The experimental validation demonstrated the superior performance of the proposed system by achieving 98.97% accuracy in DP and 98.89% accuracy in stroke analysis, outperforming existing classifiers. Also, the proposed MJCC technique attained 98.92% efficiency, surpassing the traditional encryption models. Thus, the proposed system produces a secure, scalable, and highly accurate DP and stroke analysis in ME. Further, the research will extend the approach to other CD like cancer and heart disease to improve the predictive performance.

Keywords: Chronic disease monitoring (CDM); Deep learning (DL); Diabetes prediction (DP); Fuzzy Z-log-clipping inference system (FZCIS); Internet of Things (IoT); Metaverse environment (ME) in healthcare; Probabilistic-centric optimized recurrent; Sechelliott neural network (PO-RSNN); Stroke analysis (SA); Wearable devices (WD).

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Conflict of interest statement

Declarations. Competing interests: The authors declare no competing interests.

Figures

Fig. 1
Fig. 1
Block diagram of the proposed system.
Fig. 2
Fig. 2
The structural representation of the proposed PO-RSNN.
Fig. 3
Fig. 3
Performance analysis of the proposed PO-RSNN regarding (a) accuracy, precision, and recall, (b) training time, and (c) FPR and FNR.
Fig. 4
Fig. 4
Performance analysis regarding (a) accuracy, f-measure, sensitivity, specificity, and (b) TPR and TNR.
Fig. 5
Fig. 5
Encryption and decryption time analysis.
Fig. 6
Fig. 6
Performance analysis of the proposed FZCIS.
Fig. 7
Fig. 7
Average fitness analysis.
Fig. 8
Fig. 8
Cross-validation.
Fig. 9
Fig. 9
Computational overhead analysis.
Fig. 10
Fig. 10
Complexity analysis of PO-RSNN.
Fig. 11
Fig. 11
Threat analysis for MJCC.
Fig. 12
Fig. 12
Feature distribution of the DPD (a) Before pre-processing and (b) after pre-processing.
Fig. 13
Fig. 13
Feature distribution of the SPD (a) Before pre-processing and (b) after pre-processing.
See this image and copyright information in PMC

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