TY  - JOUR
AU  - Singh, Shobhita
AU  - Aggarwal, Shubhani
AU  - Singh, Aishani
AU  - Sharma, Anupriya
PY  - 2025
DA  - 2025/09/21
TI  - Integrating Artificial Intelligence and Machine Learning in Autism Detection via Gut Microbiome Analysis
JO  - ICCK Transactions on Machine Intelligence
T2  - ICCK Transactions on Machine Intelligence
JF  - ICCK Transactions on Machine Intelligence
VL  - 1
IS  - 2
SP  - 90
EP  - 102
DO  - 10.62762/TMI.2025.682666
UR  - https://www.icck.org/article/abs/TMI.2025.682666
KW  - autism spectrum disorder (ASD)
KW  - gut microbiome
KW  - artificial intelligence
KW  - machine learning
KW  - ensemble approach
AB  - The Autism Spectrum Disorder (ASD) diagnosis and detection in its initial stages is a more complex issue in the face of the wide-ranging, diverse nature and causes. Subsequent literature inclined towards a possible correlation of gut microbiome with ASD, and its disclosure presents a more promising attribute for imminent discovery conduits. The dataset on gut microbiome associated with ASD focuses specifically on the microbial compositions obtained through 16S rRNA sequencing. This study presents a novel method that integrates Artificial Intelligence employing various Machine Learning (ML) robust classifiers such that Support Vector Machines (SVM), Random Forest, k-Nearest Neighbors (KNN), Logistic Regression, and Artificial Neural Networks (ANN), additionally PCA and k-means clustering is implemented for feature extraction to reveal important hidden patterns of ASD associated microbiomes from microbiome profiles. By integrating these model classifiers, the ensemble technique was developed to harness the strengths of each model, which enhances the dependability of the gut microbiome and offers a novel approach. The ensemble method suggested has an accuracy of 98.75%, a precision of 95.11%, a recall of 96.47% and an F1 score of 98.28% in the early determination of autism. The observational feature of this multifaceted approach not only enhances accuracy and precision but also provides a more complete picture of the role of autism spectrum disorders and eventually leads to the development of interventions and personalised approaches to these problems.
SN  - 3068-7403
PB  - Institute of Central Computation and Knowledge
LA  - English
ER  - 

@article{Singh2025Integratin,
  author = {Shobhita Singh and Shubhani Aggarwal and Aishani Singh and Anupriya Sharma},
  title = {Integrating Artificial Intelligence and Machine Learning in Autism Detection via Gut Microbiome Analysis},
  journal = {ICCK Transactions on Machine Intelligence},
  year = {2025},
  volume = {1},
  number = {2},
  pages = {90-102},
  doi = {10.62762/TMI.2025.682666},
  url = {https://www.icck.org/article/abs/TMI.2025.682666},
  abstract = {The Autism Spectrum Disorder (ASD) diagnosis and detection in its initial stages is a more complex issue in the face of the wide-ranging, diverse nature and causes. Subsequent literature inclined towards a possible correlation of gut microbiome with ASD, and its disclosure presents a more promising attribute for imminent discovery conduits. The dataset on gut microbiome associated with ASD focuses specifically on the microbial compositions obtained through 16S rRNA sequencing. This study presents a novel method that integrates Artificial Intelligence employing various Machine Learning (ML) robust classifiers such that Support Vector Machines (SVM), Random Forest, k-Nearest Neighbors (KNN), Logistic Regression, and Artificial Neural Networks (ANN), additionally PCA and k-means clustering is implemented for feature extraction to reveal important hidden patterns of ASD associated microbiomes from microbiome profiles. By integrating these model classifiers, the ensemble technique was developed to harness the strengths of each model, which enhances the dependability of the gut microbiome and offers a novel approach. The ensemble method suggested has an accuracy of 98.75\%, a precision of 95.11\%, a recall of 96.47\% and an F1 score of 98.28\% in the early determination of autism. The observational feature of this multifaceted approach not only enhances accuracy and precision but also provides a more complete picture of the role of autism spectrum disorders and eventually leads to the development of interventions and personalised approaches to these problems.},
  keywords = {autism spectrum disorder (ASD), gut microbiome, artificial intelligence, machine learning, ensemble approach},
  issn = {3068-7403},
  publisher = {Institute of Central Computation and Knowledge}
}