P. Karthikeyan

Work place: Dept. of Computer Science and Information Engineering, National Chung Cheng University, Chiayi, Taiwan-62102

E-mail: karthi@ccu.edu.tw


Research Interests:


P. Karthikeyan obtained his the Bachelor of Engineering (B.E.,) in Computer Science and Engineering from Anna University, Chennai, and Tamil nadu, India in 2005 and received his Master of Engineering (M.E,) in Computer Science and Engineering from Anna University, Coimbatore India in 2009. He has completed Ph.D. degree in Anna University, Chennai in 2018. Skilled in developing projects and carrying out research in the area of Cloud computing and Data science with the programming skill in Java, Python, R and C. He published more than 30 International journals with good impact factor and presented more than 20 International conferences. He was the reviewer of Elsevier, Springer, Inderscience and reputed Scopus indexed journals. He is acting as editorial board members in EAI Endorsed Transactions on Energy Web. The International Arab Journal of Information Technology and Blue Eyes Intelligence Engineering and Sciences Publication journal.

Author Articles
Optimized Feature Selection and Transformations for Early Stage Prediction of Autism Using Supervised Machine Learning Models

By Praveena K N R Mahalakshmi Manjunath C Ahmad Faiz Zubair P. Karthikeyan

DOI: https://doi.org/10.5815/ijmecs.2023.06.06, Pub. Date: 8 Dec. 2023

Autism Spectrum Disorder (ASD) is a neurodevelopmental syndrome which cannot be curable but can be predicted in early stage. Early prediction and cure may help to diagnose the autism. In existing methods, prediction of best feature is not identified for detecting the autism in early stage. In this proposed research, prediction of ASD has been done by identifying the best feature transformation technique with best ML classifier and finding out the most significant feature for diagnosis of autism in early age. Early-detected ASD datasets pertaining to toddler and child are collected and applied few Feature transformation techniques, comprising log, power-box-cox and yeo-Johnson transformations to these datasets. Then, using these ASD datasets, several classification approaches were applied, and their efficiency was evaluated. Adaboost given 100% accuracy for toddler dataset and whereas, Random forest showed 98.3% accuracy for child datasets. The feature transformations ensuing the best prediction was Log, Power- Box cox and Yeo-Johnson Transformation for toddler and Log transformation for children datasets. After these exploration, various feature selection techniques like univariate (UNI) and recursive feature elimination (RFE) are applied to these transformed datasets to recognize the most significant ASD risk feature to predict the autism in early stage for toddler and child data. It is found that A5 feature is most significant feature for toddler, A4 stands most significant feature for child based on univariate and RFE. This benefits the doctor to provide the suitable diagnosis in their early stage of life. The results of these logical methodologies show that ML methods can yield precise predictions of ASD when they are accurately optimised. This shows that using these models for early ASD detection may be feasible.

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