Existing Trends in Mental Health Based on IoT Applications: A Systematic Review

Ali, Nizamani Muhammad; Ali, M; Brohi, Pirah

doi:10.1002/9781119761655.ch12

The Smart Cyber Ecosystem for Sustainable Development 2021

DOI: 10.1002/9781119761655.ch12

|View full text |Cite

Existing Trends in Mental Health Based on IoT Applications: A Systematic Review

Nizamani Muhammad Ali

M Ali

Pirah Brohi

Help me understand this report

Search citation statements

Order By: Relevance

Paper Sections

Select...

Citation Types

Supporting

Mentioning

Contrasting

Year Published

2022

Publication Types

Select...

Article1

Relationship

Self Cite0

Independent1

Authors

Journals

Cited by 1 publication

References 30 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

Mental Health Monitoring and Detection Based on Machine Learning and IoT Data

Mohsen¹,

Al-Khaleel²,

Alfadhly³

2022

Int. J. Info. Tech. Dec. Mak.

View full text Add to dashboard Cite

Recently, the Internet of Things (IoT) has become a buzzword in various technology fields because of its many applications. Healthcare is one of the most important fields in daily life and holds significant interest for IoT and artificial intelligence researchers. In the area of healthcare, the mental healthcare field has attracted many researchers and funding organizations for various reasons. Among those reasons are the adverse impact of mental conditions on both individuals and society, the high costs of mental care, the nature of mental conditions and their hidden and unclear symptoms, and the stigma associated with them. In this work, an approach to building an IoT mental health monitoring and detection system has been proposed. The depression problem is used as a case study for the mental conditions. The proposed approach involves data collection, data pre-processing, feature extraction, feature selection, and model building. Machine learning (ML) and data mining techniques, along with different data types for training and testing the models, have been utilized to accomplish the tasks of the proposed approach. Four ML algorithms have been investigated to detect and predict the diagnosis of the mental state. These algorithms are random forest, decision tree, support vector machine (SVM) and [Formula: see text]-nearest neighbors (KNN). Our ML detection model has achieved a high detection accuracy of 95% using the random forest algorithm. Comparisons in different aspects with other works are presented. Our work has the advantages of using ML, adopting different data types, and therefore achieving better classification.

show abstract

Mental Health Monitoring and Detection Based on Machine Learning and IoT Data

Mohsen¹,

Al-Khaleel²,

Alfadhly³

2022

Int. J. Info. Tech. Dec. Mak.

View full text Add to dashboard Cite

show abstract

scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.

Contact Info

customersupport@researchsolutions.com

10624 S. Eastern Ave., Ste. A-614

Henderson, NV 89052, USA

This site is protected by reCAPTCHA and the Google Privacy Policy and Terms of Service apply.

Blog Terms and Conditions API Terms Privacy Policy Contact Cookie Preferences Do Not Sell or Share My Personal Information

Made with 💙 for researchers

Part of the Research Solutions Family.

Existing Trends in Mental Health Based on IoT Applications: A Systematic Review

Cited by 1 publication

References 30 publications

Mental Health Monitoring and Detection Based on Machine Learning and IoT Data

Mental Health Monitoring and Detection Based on Machine Learning and IoT Data

Contact Info

Product

Resources

About