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Table 1 Comparing the advantages and disadvantages of previous methods

From: Remote patient monitoring and classifying using the internet of things platform combined with cloud computing

References

Method

Disadvantage

Advantage

[4]

An enhanced optimized Genetic Algorithm feature selection technique is applied to provide relevant information from a high-dimensional Anopheles Gambia dataset and test its classification by SVM-Kernel algorithms

Complicated model

High accuracy in classification

[5]

Provide a patient remote monitoring system with the aim of effectively managing hospital resources through patient monitoring at home

Insufficient accuracy of diagnostic information, high classification error

High classification speed, removing outliers, using IoT

[10]

An innovative IoT-based system for identifying medications and monitoring prescription medication

Insufficient accuracy of diagnostic information

high classification error

High computational speed, simple model, removing outliers

[11]

A remote monitoring and decision support system to assist physicians in diagnosing, remote monitoring, treating, prescribing, rehabilitating, and advancing patients with Parkinson’s disease

Complicated model, long computation time

High classification accuracy, simple model

[12]

An IoT-based health monitoring system considering the role of smart data in the smart home for patient-centered remote health monitoring

Insufficient accuracy of diagnostic information, high classification error

High classification accuracy

[13]

An IoT-based mobile gateway (e.g. mobile/tablet /PDA, etc.) for health scenarios

Complicated model, long computation time

High accuracy in classification

[14]

IoT-based health monitoring system for children with Autism

Complicated model, long computation time

High accuracy in classification

[15]

An industrial IoT-based monitoring framework for healthcare

High error, complicated model and low computational speed

Optimal accuracy in classification

[16]

Smart Architecture for In-Home Healthcare

Insufficient accuracy of diagnostic information, high classification error

High computational speed, simple model, removing outliers

[17]

An IoT-based patient monitoring framework in the intensive care unit

Complicated model, long computation time

High accuracy in classification

[18]

Medication reminder and monitoring system for health using IoT

Insufficient accuracy of diagnostic information, high classification error

High computational speed, simple model, removing outliers

[19]

IoT based patient monitoring system

Complicated model, long computation time

High accuracy in classification

[20]

Patient monitoring system using IoT

Insufficient accuracy of diagnostic information, high classification error

High accuracy in classification

[21]

A smart patient monitoring system to monitor patients’ health

High error, complicated model and low computational speed

Optimal classification speed, removing outliers and using IoT platform

[22]

New generation technology and IoT for managing and analyzing big data

Insufficient accuracy of diagnostic information, high classification error

Optimal accuracy in classification

[23]

An IoT-based healthcare monitoring and analysis system

High error, complicated model and low computational speed

Optimal accuracy in classification

[24]

Approach uses a long-term and short-term memory network and extends it to two mechanisms (i.e., time and attention-based)

long computational speed, complicated model

Optimal accuracy in classification

[25]

A comprehensive analysis of Long-Short Term Memory (LSTM) based DL models

Long computational speed

High classification accuracy

[26]

A novel optimized hybrid investigative combines an optimized genetic algorithm with Principal Component Analysis and Independent Component Analysis (GA-O-PCA and GAO-ICA)

Complicated model

Optimal classification accuracy

[27]

Using clustering techniques, deep neural networks, online hybrid similarity criteria as a method for analysis

Long computation time

High classification accuracy