Machine Learning in E-health: A Comprehensive Survey of Anxiety

Bibi Nushrina Teelhawod, Faijan Akhtar, Md Belal Bin Heyat, Pragati Tripathi, Rajat Mehrotra, Ashamo Betelihem Asfaw, Omar Al Shorman, Mahmoud Masadeh

2021 International Conference on Data Analytics for Business and Industry (ICDABI) · 2021 · 28 citations · 29 references

Concepts

Abstract

Anxiety is the sixth major disorder that arises due to sleeplessness, inspiration, energy, hunger loss and desperate ideas. This study aims to define the extent of the research carried on anxiety with machine learning (ML) and to generate a database. We highlight serious problems for additional research obligations and communications. This paper designs a perception-based image of ML We have investigated deep insights of material to re-claim the current understanding of anxiety with ML from engineering detection to clinical treatment. We found 18 research articles and 28 closest keywords in the Web of Science (WoS) database. In addition, we extracted the information including objective, subject, major, scoring technique, classifier, and performance measure from previously published 18 articles. This study has discovered the research gap and future direction, so far focused on anxiety with ML. In addition, it also delivers objectives for the upcoming work to be competent in a significant and scientific manner.

References

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