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An adaptive secure internet of things and cloud based disease classification strategy for smart healthcare industry

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Abstract

Hospital facilities were limited in rural areas and there is no awareness about disease infection and so on. Hence, the Internet of Things (IoT) technology was designed in the health care industry to treat and save illiterate people from the harmful diseases. Recently, the health care system based on IoT technology became a huge demand in the online and medical industry. However, offering the protection frame for gathered data in cloud becomes a challenging task, because the cloud contains a lot of different patient data. To overcome this issue, the current research has designed a novel Elapid Encryption in cloud frame to secure the gathered data. Moreover, the security function is executed by encrypting the collected information in the cloud storage. Also, a novel generalized fuzzy intelligence and ant lion optimization model was developed for disease prediction and severity calculation. Hence, the developed design is implemented using MATLAB and its efficiency is compared with the existing approaches such as H-DT, DNN, and DTNNN. From the comparison, proposed model has finest and highest performance like high accuracy, precision, recall and confidential rate then lower error rate and processing time. Consequently, AUC value by the developed model is 89.8%, sensitivity rate as 99% and specificity rate as 97.8%, less error rate as 0.08, accuracy rate as 99.92% and 99.9% of precision, high recall measure as 99.92%, time consumption of the proposed model is 10 s.

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Abbreviations

IoT:

Internet of Things

EE:

Elapid encryption

GFI-ALO:

Generalized fuzzy intelligence and ant lion optimization

DoS:

Denial of service

ALO:

Ant lion optimization

AUC:

Area under curve

H-DT:

Hybrid–decision tree

DNN:

Deep neural network

DTNNN:

Deep trained neocognitron neural network

DESRP:

Data encryption standard based register permutation

ESV-AES:

Enhanced-Small Scale Variant with Advanced Encryption Standard

EBA:

Enhanced Blowfish Algorithm

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Authors and Affiliations

  1. Department of Computer Applications, KIET Group of Institutions, Ghaziabad, Uttar Pradesh, 201206, India

    Ankit Verma

  2. Department of Computer Science & Engineering, Invertis University, Bareilly, Uttar Pradesh, 243123, India

    Ankit Verma & Gaurav Agarwal

  3. Department of Computer Applications, KIET Group of Institutions, Delhi-NCR, Ghaziabad, India

    Amit Kumar Gupta, Vipin Kumar & Shweta Singh

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  1. Ankit Verma
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Verma, A., Agarwal, G., Gupta, A.K. et al. An adaptive secure internet of things and cloud based disease classification strategy for smart healthcare industry. Wireless Netw 31, 879–897 (2025). https://doi.org/10.1007/s11276-024-03783-5

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