abstract: 'Background : Socio-behavioral disorders(SBD), a subtype of

  neurodevelopmental disorders (NDDs) characterized by social and

  behavioral abnormalities, is a significant mental health concern

  requiring immediate attention. Phenotypic knowledge, biological

  understanding and the tools developed are all from western countries.

  Numerous researches have been conducted that have scrutinized the

  performance accuracy of traditional-based SBD tools developed in western

  culture. However, very little information is available for low or

  middle-income countries. Objective: In middle-income countries like

  India, there is a shortage of resources, trained professionals and a

  lack of knowledge regarding which tools are effective for a particular

  target group owing to which most of the cases go undetected and

  undiagnosed until adolescence. Motivated by the earlier discussion, this

  study''s objective is to consider all the pathways from traditional to

  Artificial Intelligence (AI) tools developed for diagnosing SBD in the

  Indian population. This research work expounds on the systematic study

  and analysis of various conventional and fuzzy-based expert systems

  introduced between 1925-2021. Methods: PRISMA guidelines were used to

  select the articles published on the web of science, SCOPUS, and EMBASE

  to identify relevant Indian studies. A total of 148 papers are

  considered impactful for SBD prediction using traditional or fuzzy-based

  techniques. This survey deliberated the work done by the different

  researchers, highlighting the limitations in the existing literature and

  the performance comparison of tools based on various parameters such as

  accuracy, sensitivity, specificity, target audience, along with their

  pros and cons. Some investigations have been designed, and the solutions

  to those were explored. Results : Results of this study indicated that

  most validated SBD tools present many barriers to use in the Indian

  population. Thus, to overcome these implications, an Artificial

  Intelligence(AI) framework, MRIMMTL, based on MRI multimodality transfer

  learning techniques(TL), is proposed to be implemented for the early

  detection of SBD subjects. (c) 2022 Elsevier B.V. All rights reserved.'
affiliation: 'Mengi, M (Corresponding Author), Cent Univ, Dept Comp Sci \& Informat
  Technol, Jammu 181143, India.

  Mengi, Mehak; Malhotra, Deepti, Cent Univ, Dept Comp Sci \& Informat Technol, Jammu
  181143, India.'
article-number: '109633'
author: Mengi, Mehak and Malhotra, Deepti
author-email: '0550519.csit@cujammu.ac.in

  deepti.csit@cujammu.ac.in'
author_list:
- family: Mengi
  given: Mehak
- family: Malhotra
  given: Deepti
da: '2023-09-28'
doi: 10.1016/j.asoc.2022.109633
earlyaccessdate: SEP 2022
eissn: 1872-9681
files: []
issn: 1568-4946
journal: APPLIED SOFT COMPUTING
keywords: 'Socio-behavioral disorders; Neurodevelopmental disorders; Autism

  spectrum disorder; Attention deficit hyperactivity disorder; ASD; ADHD;

  Artificial intelligence; Fuzzy tools; Soft computing; Transfer learning;

  Domain adaptation; Screening tools; Diagnostic tools; Biomarkers'
keywords-plus: 'AUTISM SPECTRUM DISORDER; CHILD-BEHAVIOR-CHECKLIST; HIGH-FUNCTIONING

  AUTISM; FUZZY COGNITIVE MAPS; ADHD RATING-SCALE; SCREENING TOOL;

  ASPERGERS-DISORDER; 2-YEAR-OLDS STAT; YOUNG-CHILDREN; PRIMARY-CARE'
language: English
month: NOV
number-of-cited-references: '152'
papis_id: c826edb51ec99c93bdbb8d3aa5b9f6c8
ref: Mengi2022systematicliterature
tags:
- review
times-cited: '1'
title: 'A systematic literature review on traditional to artificial intelligence based
  socio-behavioral disorders diagnosis in India: Challenges and future perspectives'
type: article
unique-id: WOS:000914071400001
usage-count-last-180-days: '4'
usage-count-since-2013: '5'
volume: '129'
web-of-science-categories: 'Computer Science, Artificial Intelligence; Computer Science,

  Interdisciplinary Applications'
year: '2022'