Table 1 Literature survey of IoT-based healthcare monitoring model.

From: Deep convolutional neural network based archimedes optimization algorithm for heart disease prediction based on secured IoT enabled health care monitoring system

Author

Methods

Datasets

Platform

Advantages

Limitations

Khan et al.19

SMSH

Wider-face dataset

MATLAB simulation

Better security with minimum execution time

Lack of adequate medical information

Manogaran et al.20

GC-MFR

Cleveland heart disease database

JAVA

Better detection accuracy and minimum task completion time

Data threats with higher cost

Kaur et al.26

Random forest (RF)

Public datasets

WEKA open-source tool

97.26% accuracy and minimum cost

Improper security

Wu et al.21

Deep learning

Real-time dataset

MATLAB

Minimum overfitting and enhanced accuracy

Vanishing gradient, higher computational complexity, and cost

Zhu et al.27

Edge-fog computing framework

Gliomas datasets

Azure cloud

Smaller latencies with higher accuracy

Does not promote gradual analysis

Juyal et al.28

AI-enabled cloud-based IoT

Skin image dataset

MATLAB

Improved accuracy and detection rate

Lots of security threats