World's Best Scientists 2026 revealed!

D-Index & Metrics

Computer Science

D-Index
38
Citations
8437
World Ranking
10052
National Ranking
25

Overview

Fadi Thabtah is affiliated with Manukau Institute of Technology in New Zealand. Their research spans the fields of computer science and medicine, with a focus on subfields including artificial intelligence, psychiatry and mental health, cognitive neuroscience, health information management, and education. The scientist's publication record features 32 works in computer science and 22 in medicine.

Their work addresses a range of topics, notably dementia and cognitive impairment research, autism spectrum disorder research, machine learning in healthcare, and artificial intelligence applied to healthcare. Other significant areas include child development and digital technology, imbalanced data classification techniques, and COVID-19 diagnosis using AI.

Frequent publication venues for their research include:

  • Health and Technology
  • Journal of Information & Knowledge Management
  • Intelligent Decision Technologies
  • Technology and Health Care
  • Health Information Science and Systems

Fadi Thabtah has frequently collaborated with researchers such as Firuz Kamalov, David Peebles, Robinson Spencer, Neda Abdelhamid, and Joan Lu. These collaborations have contributed to both the depth and breadth of their research output.

Some of the recent academic papers authored or co-authored by Fadi Thabtah include:

  • Least Loss: A simplified filter method for feature selection, 2020, Information Sciences
  • Dementia medical screening using mobile applications: A systematic review with a new mapping model, 2020, Journal of Biomedical Informatics

Other influential papers in related work by co-authors or contemporaries in the field include:

  • Exploring feature selection and classification methods for predicting heart disease, 2020, Digital Health
  • Autism AI: a New Autism Screening System Based on Artificial Intelligence, 2020, Cognitive Computation
  • Feature Selection in Imbalanced Data, 2022, Annals of Data Science

Best Publications

  • Data imbalance in classification: Experimental evaluation

    Fadi A. Thabtah;Suhel Hammoud;Firuz Kamalov;Amanda H. Gonsalves

  • Phishing detection based Associative Classification data mining

    Neda Abdelhamid;Aladdin Ayesh;Fadi A. Thabtah

  • Predicting phishing websites based on self-structuring neural network

    Rami M. Mohammad;Fadi Thabtah;Lee Mccluskey

  • A review of associative classification mining

    Fadi Thabtah

  • MMAC: a new multi-class, multi-label associative classification approach

    F.A. Thabtah;P. Cowling;Yonghong Peng

  • A machine learning framework for sport result prediction

    Rory P. Bunker;Fadi Thabtah

  • MCAR: multi-class classification based on association rule

    F. Thabtah;P. Cowling;Y. Peng

  • Machine learning in autistic spectrum disorder behavioral research: A review and ways forward

    Fadi Thabtah

  • Intelligent rule-based phishing websites classification

    Rami Mustafa A. Mohammad;Fadi A. Thabtah;Lee McCluskey

  • Intelligent phishing detection system for e-banking using fuzzy data mining

    Maher Aburrous;M. A. Hossain;Keshav Dahal;Fadi Thabtah

  • A new machine learning model based on induction of rules for autism detection.

    Fadi A. Thabtah;David Peebles

  • An assessment of features related to phishing websites using an automated technique

    R. M. Mohammad;F. Thabtah;L. McCluskey

  • Autism Spectrum Disorder Screening: Machine Learning Adaptation and DSM-5 Fulfillment

    Fadi Thabtah

  • Exploring feature selection and classification methods for predicting heart disease.

    Robinson Spencer;Fadi Thabtah;Neda Abdelhamid;Michael Thompson

  • A new computational intelligence approach to detect autistic features for autism screening.

    Fadi A. Thabtah;Firuz Kamalov;Khairan Rajab

  • Tutorial and critical analysis of phishing websites methods

    Rami M. Mohammad;Fadi Thabtah;Lee McCluskey

  • An accessible and efficient autism screening method for behavioural data and predictive analyses.

    Fadi A. Thabtah

  • Predicting Phishing Websites Using Classification Mining Techniques with Experimental Case Studies

    Maher Aburrous;M. A. Hossain;Keshav Dahal;Fadi Thabtah

  • A machine learning autism classification based on logistic regression analysis

    Fadi A. Thabtah;Neda Abdelhamid;David Peebles

  • Prediction of Coronary Heart Disease using Machine Learning: An Experimental Analysis

    Amanda H. Gonsalves;Fadi Thabtah;Rami Mustafa A. Mohammad;Gurpreet Singh

  • A recent review of conventional vs. automated cybersecurity anti-phishing techniques

    Issa Qabajeh;Fadi A. Thabtah;Francisco Chiclana

  • Naïve Bayesian Based on Chi Square to Categorize Arabic Data

    Fadi Thabtah;Mohammad Ali

Frequent Co-Authors

Peter I. Cowling
Peter I. Cowling Queen Mary University of London
Francisco Chiclana
Francisco Chiclana De Montfort University
Keshav Dahal
Keshav Dahal University of the West of Scotland

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