Ahmad Faiz Zubair

Work place: School of Mechanical Engineering, College of Engineering, Universiti Teknologi Mara, Kampus Pulau Pinang, 13500 Permatang Pauh, Pulau Pinang, Malaysia

E-mail: ahmadfaiz@uitm.edu.my


Research Interests: Technology literacy and integration


Ahmad Faiz Zubair received his Ph.D. in CAD/CAM Engineering from the Universiti Sains Malaysia in 2019. He received his MSc in CAED Engineering from Strathclyde University, Glasgowin 2009and Bachelor of engineering in CAD/CAM Engineering from University of Malaya in 2006. He is currently a Senior Lecturer in the Faculty of Mechanical Engineering Universiti Teknologi MARA,Penang Branch. There he is a member of the Intelligent and Sustainable Manufacturing Centre, and the advisor of Student’s Boiler and Safety Club. His research interests include CAD/CAM, CAPP, Manufacturing Technology and CNC Machining.He is also a register Chartered Engineerof IMechE United Kingdomandan active ProfessionalTechnologist of MalaysiaBoard of Technologist. He is a Certified Solidworks Associated (CSWA) and certified CATIA Mechanical DesignAssociate.

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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