PMID- 38464462
OWN - NLM
STAT- PubMed-not-MEDLINE
LR  - 20240312
IS  - 2047-2501 (Print)
IS  - 2047-2501 (Electronic)
IS  - 2047-2501 (Linking)
VI  - 12
IP  - 1
DP  - 2024 Dec
TI  - Autism spectrum disorder detection with kNN imputer and machine learning 
      classifiers via questionnaire mode of screening.
PG  - 18
LID - 10.1007/s13755-024-00277-8 [doi]
LID - 18
AB  - Autism spectrum disorder (ASD) is a neurodevelopmental disorder. ASD cannot be 
      fully cured, but early-stage diagnosis followed by therapies and rehabilitation 
      helps an autistic person to live a quality life. Clinical diagnosis of ASD 
      symptoms via questionnaire and screening tests such as Autism Spectrum 
      Quotient-10 (AQ-10) and Quantitative Check-list for Autism in Toddlers (Q-chat) 
      are expensive, inaccessible, and time-consuming processes. Machine learning (ML) 
      techniques are beneficial to predict ASD easily at the initial stage of 
      diagnosis. The main aim of this work is to classify ASD and typical developed 
      (TD) class data using ML classifiers. In our work, we have used different ASD 
      data sets of all age groups (toddlers, adults, children, and adolescents) to 
      classify ASD and TD cases. We implemented One-Hot encoding to translate 
      categorical data into numerical data during preprocessing. We then used kNN 
      Imputer with MinMaxScaler feature transformation to handle missing values and 
      data normalization. ASD and TD class data is classified using Support vector 
      machine, k-nearest-neighbor (KNN), random forest (RF), and artificial neural 
      network classifiers. RF gives the best performance in terms of the accuracy of 
      100% with different training and testing data split for all four types of data 
      sets and has no over-fitting issue. We have also examined our results with 
      already published work, including recent methods like Deep Neural Network (DNN) 
      and Convolution Neural Network (CNN). Even using complex architectures like DNN 
      and CNN, our proposed methods provide the best results with low-complexity 
      models. In contrast, existing methods have shown accuracy upto 98% with log-loss 
      upto 15%. Our proposed methodology demonstrates the improved generalization for 
      real-time ASD detection during clinical trials.
CI  - (c) The Author(s), under exclusive licence to Springer Nature Switzerland AG 2024. 
      Springer Nature or its licensor (e.g. a society or other partner) holds exclusive 
      rights to this article under a publishing agreement with the author(s) or other 
      rightsholder(s); author self-archiving of the accepted manuscript version of this 
      article is solely governed by the terms of such publishing agreement and 
      applicable law.
FAU - Shrivastava, Trapti
AU  - Shrivastava T
AUID- ORCID: 0000-0001-8455-282X
AD  - Department of Information Technology, Indian Institute of Information Technology, 
      Allahabad, Prayagraj, Uttar Pradesh 211015 India.
FAU - Singh, Vrijendra
AU  - Singh V
AD  - Department of Information Technology, Indian Institute of Information Technology, 
      Allahabad, Prayagraj, Uttar Pradesh 211015 India.
FAU - Agrawal, Anupam
AU  - Agrawal A
AD  - Department of Information Technology, Indian Institute of Information Technology, 
      Allahabad, Prayagraj, Uttar Pradesh 211015 India.
LA  - eng
PT  - Journal Article
DEP - 20240306
PL  - England
TA  - Health Inf Sci Syst
JT  - Health information science and systems
JID - 101638060
PMC - PMC10917726
OTO - NOTNLM
OT  - Artificial neural network
OT  - Autism spectrum disorder
OT  - K-nearest neighbor
OT  - Questionnaire mode of screening
OT  - Random forest
OT  - Support vector machine
OT  - kNN imputer
COIS- Conflict of interestThe authors have no competing interests relevant to the 
      content of this work.
EDAT- 2024/03/11 06:43
MHDA- 2024/03/11 06:44
PMCR- 2025/12/01
CRDT- 2024/03/11 04:35
PHST- 2023/02/16 00:00 [received]
PHST- 2024/01/17 00:00 [accepted]
PHST- 2025/12/01 00:00 [pmc-release]
PHST- 2024/03/11 06:44 [medline]
PHST- 2024/03/11 06:43 [pubmed]
PHST- 2024/03/11 04:35 [entrez]
AID - 277 [pii]
AID - 10.1007/s13755-024-00277-8 [doi]
PST - epublish
SO  - Health Inf Sci Syst. 2024 Mar 6;12(1):18. doi: 10.1007/s13755-024-00277-8. 
      eCollection 2024 Dec.