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

Web-based and Mobile Health Interventions (3114) Machine Learning (1385) Artificial Intelligence (1198) Hearing Impairment and Hearing Loss (64) Digital Health, Telehealth and e-Innovation in Clinical Settings (344) Clinical Information and Decision Making (1328)

Published on in Vol 23, No 11 (2021): November

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/28999, first published .
Exploratory Data Mining Techniques (Decision Tree Models) for Examining the Impact of Internet-Based Cognitive Behavioral Therapy for Tinnitus: Machine Learning Approach

Exploratory Data Mining Techniques (Decision Tree Models) for Examining the Impact of Internet-Based Cognitive Behavioral Therapy for Tinnitus: Machine Learning Approach

Exploratory Data Mining Techniques (Decision Tree Models) for Examining the Impact of Internet-Based Cognitive Behavioral Therapy for Tinnitus: Machine Learning Approach

Authors of this article:

Hansapani Rodrigo1, 2 Author Orcid Image ;   Eldré W Beukes2, 3, 4 Author Orcid Image ;   Gerhard Andersson5, 6 Author Orcid Image ;   Vinaya Manchaiah2, 3, 7, 8 Author Orcid Image
Article Authors Cited by (13) Tweetations (8) Metrics

Journals

  1. Manchaiah V, Nisha K, Prabhu P, Granberg S, Karlsson E, Andersson G, Beukes E. Examining the consequences of tinnitus using the multidimensional perspective. Acta Oto-Laryngologica 2022;142(1):67 View
  2. Rodrigo H, Beukes E, Andersson G, Manchaiah V. Predicting the Outcomes of Internet-Based Cognitive Behavioral Therapy for Tinnitus: Applications of Artificial Neural Network and Support Vector Machine. American Journal of Audiology 2022;31(4):1167 View
  3. Puga C, Schleicher M, Niemann U, Unnikrishnan V, Boecking B, Brueggemann P, Simoes J, Langguth B, Schlee W, Mazurek B, Spiliopoulou M. Juxtaposing Medical Centers Using Different Questionnaires Through Score Predictors. Frontiers in Neuroscience 2022;16 View
  4. Hosoe J, Sunagawa J, Nakaoka S, Koseki S, Koyama K. Data mining for prediction and interpretation of bacterial population behavior in food. Frontiers in Food Science and Technology 2022;2 View
  5. Salvi R, Chen G, Manohar S. Hyperacusis: Loudness intolerance, fear, annoyance and pain. Hearing Research 2022;426:108648 View
  6. Cardon E, Jacquemin L, Schecklmann M, Langguth B, Mertens G, Vanderveken O, Lammers M, Van de Heyning P, Van Rompaey V, Gilles A. Random Forest Classification to Predict Response to High-Definition Transcranial Direct Current Stimulation for Tinnitus Relief: A Preliminary Feasibility Study. Ear & Hearing 2022;43(6):1816 View
  7. Wu T, Wei Y, Wu J, Yi B, Li H. Logistic regression technique is comparable to complex machine learning algorithms in predicting cognitive impairment related to post intensive care syndrome. Scientific Reports 2023;13(1) View
  8. Balan J, Rodrigo H, Saxena U, Mishra S. Explainable machine learning reveals the relationship between hearing thresholds and speech-in-noise recognition in listeners with normal audiograms. The Journal of the Acoustical Society of America 2023;154(4):2278 View
  9. Yang T, Chen Y, Cheng Y, Huang J, Wu C, Chu Y. Optimizing age-related hearing risk predictions: an advanced machine learning integration with HHIE-S. BioData Mining 2023;16(1) View
  10. Kasraei B, Schmidt M, Zhang J, Bulmer C, Filatow D, Arbor A, Pennell T, Heung B. A framework for optimizing environmental covariates to support model interpretability in digital soil mapping. Geoderma 2024;445:116873 View
  11. Zhou Y, Zhang Z, Li Q, Mao G, Zhou Z. Construction and validation of machine learning algorithm for predicting depression among home-quarantined individuals during the large-scale COVID-19 outbreak: based on Adaboost model. BMC Psychology 2024;12(1) View
  12. Yin Z, Kuang Z, Zhang H, Guo Y, Li T, Wu Z, Wang L. Explainable AI Method for Tinnitus Diagnosis via Neighbor-Augmented Knowledge Graph and Traditional Chinese Medicine: Development and Validation Study. JMIR Medical Informatics 2024;12:e57678 View