This paper is in the following e-collection/theme issue:

Machine Learning (1984) Mobile Health (mhealth) (3361) Autism Spectrum Disorder (ASD) (138) Health Services in Resource-Poor Settings and LMICs (502) Artificial Intelligence (2006) mHealth for Symptom and Disease Monitoring, Chronic Disease Management (1678) mHealth in the Developing World/LMICs, Underserved Communities, and for Global Health (684)

Published on in Vol 9, No 6 (2021): June

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/29242, first published .
Informing Developmental Milestone Achievement for Children With Autism: Machine Learning Approach

Informing Developmental Milestone Achievement for Children With Autism: Machine Learning Approach

Informing Developmental Milestone Achievement for Children With Autism: Machine Learning Approach

Authors of this article:

Munirul M Haque1 Author Orcid Image ;   Masud Rabbani2 Author Orcid Image ;   Dipranjan Das Dipal2 Author Orcid Image ;   Md Ishrak Islam Zarif2 Author Orcid Image ;   Anik Iqbal2 Author Orcid Image ;   Amy Schwichtenberg3 Author Orcid Image ;   Naveen Bansal4 Author Orcid Image ;   Tanjir Rashid Soron5 Author Orcid Image ;   Syed Ishtiaque Ahmed6 Author Orcid Image ;   Sheikh Iqbal Ahamed2 Author Orcid Image
Article Authors Cited by (20) Tweetations (4) Metrics

Journals

  1. Ramachandran A, John Bosco Martin V. Adaptive autism behavior prediction using improved binary whale optimization technique. Concurrency and Computation: Practice and Experience 2023;35(3) View
  2. Schwichtenberg A, Janis A, Lindsay A, Desai H, Sahu A, Kellerman A, Chong P, Abel E, Yatcilla J. Sleep in Children with Autism Spectrum Disorder: A Narrative Review and Systematic Update. Current Sleep Medicine Reports 2022;8(4):51 View
  3. Valade F, Béliveau M, Breault C, Chabot B, Labelle F. Individual and cumulative risk factors in developmental language disorder: A case-control study. Clinical Child Psychology and Psychiatry 2023;28(1):398 View
  4. deLeyer‐Tiarks J, Li M, Levine‐Schmitt M, Andrade B, Bray M, Peters E. Advancing autism technology. Psychology in the Schools 2023;60(2):495 View
  5. Rabbani M, Haque M, Dipal D, Zarif M, Iqbal A, Schwichtenberg A, Bansal N, Soron T, Ahmed S, Ahamed S. A data-driven validation of mobile-based care (mCARE) project for children with ASD in LMICs. Smart Health 2022;26:100345 View
  6. Murthy D, Lee J, Dashtian H, Kong G. Influence of User Profile Attributes on e-Cigarette–Related Searches on YouTube: Machine Learning Clustering and Classification. JMIR Infodemiology 2023;3:e42218 View
  7. Zhang Y, Fong P, Yamoah Agyemang D. What Should Be Focused on When Digital Transformation Hits Industries? Literature Review of Business Management Adaptability. Sustainability 2021;13(23):13447 View
  8. Khudhur D, Khudhur S. The classification of autism spectrum disorder by machine learning methods on multiple datasets for four age groups. Measurement: Sensors 2023;27:100774 View
  9. Mendonça R, Marques G, Lione V, Grokoski K. A aplicação da Comunicação Suplementar e Alternativa para a estimulação da intenção comunicativa e da cognição em pacientes com Transtorno do Espectro Autista. Revista CEFAC 2023;25(5) View
  10. Mendonça R, Marques G, Lione V, Grokoski K. Application of Augmentative and Alternative Communication to stimulate communicative intention and cognition in patients with Autism Spectrum Disorder. Revista CEFAC 2023;25(5) View
  11. Jia Q, Wang X, Zhou R, Ma B, Fei F, Han H. Systematic bibliometric and visualized analysis of research hotspots and trends in artificial intelligence in autism spectrum disorder. Frontiers in Neuroinformatics 2023;17 View
  12. Reddy K, Taksande A, Kurian B. Harnessing the Power of Mobile Phone Technology: Screening and Identifying Autism Spectrum Disorder With Smartphone Apps. Cureus 2024 View
  13. Alam M, Sajib M, Rahman F, Ether S, Hanson M, Sayeed A, Akter E, Nusrat N, Islam T, Raza S, Tanvir K, Chisti M, Rahman Q, Hossain A, Layek M, Zaman A, Rana J, Rahman S, Arifeen S, Rahman A, Ahmed A. Implications of Big Data Analytics, AI, Machine Learning, and Deep Learning in the Health Care System of Bangladesh: Scoping Review. Journal of Medical Internet Research 2024;26:e54710 View
  14. Sridevi P, Rabbani M, Aziz M, Upama P, Mamun S, Khan R, Ahamed S. Noninvasive estimation of blood glucose and HbA1c using Quantum Machine Learning technique. Machine Learning with Applications 2025;19:100626 View
  15. Sridevi P, Arefin Z, Ahamed S. An integrated machine learning and hyperparameter optimization framework for noninvasive creatinine estimation using photoplethysmography signals. Healthcare Analytics 2025;7:100395 View
  16. Rajam F, Swakkin B. MAPLE: A Novel Processing Technique for Adult Autism Prediction. Engineering, Technology & Applied Science Research 2025;15(3):23901 View

Books/Policy Documents

  1. Rabbani M, Haque M, Das Dipal D, Zarif M, Iqbal A, Schwichtenberg A, Bansal N, Soron T, Ahmed S, Ahamed S. Pervasive Computing Technologies for Healthcare. View

Conference Proceedings

  1. Kavitha V, Siva R. 2022 International Conference on Automation, Computing and Renewable Systems (ICACRS). Review of Machine Learning Algorithms for Autism Spectrum Disorder Prediction View
  2. Sridevi P, Rabbani M, Ahamed S. 2023 IEEE International Conference on Digital Health (ICDH). A Comprehensive Study for Predicting Chronic Kidney Disease, Diabetes, Hypertension, and Anemia by Machine Learning and Feature Engineering Techniques View
  3. Sridevi P, Arefin K, Das D, Khan R, Ahamed S. 2024 IEEE 48th Annual Computers, Software, and Applications Conference (COMPSAC). Noninvasive ML-Based BUN Prediction from Ten-Second Fingertip Video Using Generative AI View