Prediction of assistance dog training outcomes using machine learning and deep learning models

Article


Amirhosseini, M., Serpell, J. A., Bray, E. E., Block, T. A., Douglas, L. E. L. C., Kennedy, B. S., Evans, K. M., Freeberg, K. and Pettigrew, P. 2025. Prediction of assistance dog training outcomes using machine learning and deep learning models. Applied Animal Behaviour Science. 287 (Art. 106632). https://doi.org/10.1016/j.applanim.2025.106632
AuthorsAmirhosseini, M., Serpell, J. A., Bray, E. E., Block, T. A., Douglas, L. E. L. C., Kennedy, B. S., Evans, K. M., Freeberg, K. and Pettigrew, P.
Abstract

This study investigates the predictive power of machine learning and deep learning models for forecasting training outcomes in assistance dogs, using behavioral survey data (C-BARQ) collected from volunteer puppy-raisers at two developmental stages: 6 months and 12 months. We used data from two assistance dog training organizations–Canine Companions and The Seeing Eye, Inc.– to assess model performance and generalizability across different training contexts. Six models, including traditional machine learning approaches (SVM, Random Forest, Decision Tree, and XGBoost) and deep learning architectures (MLP and CNN), were trained and evaluated on C-BARQ behavioral scores using metrics such as accuracy, F1 Score, precision, and recall. Results indicate that Support Vector Machine (SVM) and XGBoost consistently delivered the highest prediction accuracy, with SVM achieving up to 80 % accuracy in the Canine Companions dataset and 71 % in the Seeing Eye dataset. Although deep learning models like CNN showed moderate accuracy, traditional machine learning models excelled, particularly in structured, tabular data where feature separability is essential. Models trained on 12-month data generally yielded higher predictive accuracy than those trained on 6-month data, highlighting the value of extended behavioral observations. This research underscores the efficacy of traditional machine learning models for early-phase prediction and emphasizes the importance of aligning model selection with dataset characteristics and the stage of behavioral assessment.

KeywordsMachine learning; Deep learning; C-BARQ; Dog training; Predictive modeling; Behavior assessment; SVM; XGBoost; Working dogs
JournalApplied Animal Behaviour Science
Journal citation287 (Art. 106632)
ISSN0168-1591
1872-9045
Year2025
PublisherElsevier
Accepted author manuscript
License
File Access Level
Repository staff only
Publisher's version
License
File Access Level
Anyone
Supplemental file
License
File Access Level
Anyone
Digital Object Identifier (DOI)https://doi.org/10.1016/j.applanim.2025.106632
Publication dates
Online21 Apr 2025
Publication process dates
Submitted05 Jan 2025
Accepted15 Apr 2025
Deposited14 May 2025
Copyright holder© 2025 The Authors
Additional information

This research has been funded by Dogvatar, Inc. This paper has been selected as a VIP paper for Applied Animal Behaviour Science’s 50th anniversary special issue—a major honour in this field.

Permalink -

https://repository.uel.ac.uk/item/8z74q

Download files


Publisher's version
1-s2.0-S0168159125001303-main (1).pdf
License: CC BY 4.0
File access level: Anyone


Supplemental file
1-s2.0-S0168159125001303-mmc1.pdf
License: CC BY 4.0
File access level: Anyone

  • 21
    total views
  • 16
    total downloads
  • 9
    views this month
  • 5
    downloads this month

Export as

Related outputs

Integrated Sentiment and Emotion Analysis of the Ukraine-Russia Conflict Using Machine Learning and Transformer Models
Amirhosseini, M., Berardinelli, N., Gaikwad, K., Iwuchukwu, C. E. and Ahmed, M. 2025. Integrated Sentiment and Emotion Analysis of the Ukraine-Russia Conflict Using Machine Learning and Transformer Models. International Conference on Data Science, Technology and Applications. Bilbao - Spain 10 - 12 Jun 2025 SciTePress.
WASPO: Workload-Aware Spark Performance Optimization Using NSGA-II
Karami, A. and Amirhosseini, M. 2025. WASPO: Workload-Aware Spark Performance Optimization Using NSGA-II. Cognitive Models and Artificial Intelligence Conference. Prague-Czech Republic 13 - 14 Jun 2025 IEEE.
Advancing Personality Type Prediction: Utilizing Enhanced Machine and Deep Learning Models with the Myers-Briggs Type Indicator
Amirhosseini, M., Karami, A. and Kalabi, F. 2025. Advancing Personality Type Prediction: Utilizing Enhanced Machine and Deep Learning Models with the Myers-Briggs Type Indicator. Cognitive Models and Artificial Intelligence Conference. Prague-Czech Republic 13 - 14 Jun 2025 IEEE.
Harnessing Social Media Sentiment for Predictive Insights into the Nigerian Presidential Election
Alao, J. O., Amirhosseini, M., Karami, A. and Ghorashi, S. A. 2025. Harnessing Social Media Sentiment for Predictive Insights into the Nigerian Presidential Election. Cognitive Models and Artificial Intelligence Conference. Prague-Czech Republic 13 - 14 Jun 2025 IEEE.
AI-Driven Mortality Prediction in COVID-19 Patients Using Advanced Feature Selection
Rajakaruna, I., Amirhosseini, M., Li, Y., Karami, A. and Arachchillage, D. J. 2025. AI-Driven Mortality Prediction in COVID-19 Patients Using Advanced Feature Selection. Cognitive Models and Artificial Intelligence Conference. Prague-Czech Republic 13 - 14 Jun 2025 IEEE.
Comparison of 7 Artificial Intelligence models in Predicting Venous Thromboembolism in COVID-19 Patients
Rajakaruna, I., Amirhosseini, M., Makris, M., Laffan, M., Li, Y. and Arachchillage, D. J. 2025. Comparison of 7 Artificial Intelligence models in Predicting Venous Thromboembolism in COVID-19 Patients. Research and Practice in Thrombosis and Haemostasis. 9 (2), p. Art. 102711. https://doi.org/10.1016/j.rpth.2025.102711
Breaking Down SEO Complexity: Bridging PCA and Bayesian-Optimized t-SNE
Karami, A., Ghasemabadi, S. F. and Amirhosseini, M. 2024. Breaking Down SEO Complexity: Bridging PCA and Bayesian-Optimized t-SNE. 2024 IEEE International Conference on Big Knowledge (ICBK). IEEE. https://doi.org/10.1109/ICKG63256.2024.00028
Utilizing machine Learning Techniques to Predict State-of-Charge in Li-ion Batteries
Khatri, A., Lota, J., Nepal, P. and Amirhosseini, M. H. 2024. Utilizing machine Learning Techniques to Predict State-of-Charge in Li-ion Batteries. IS'24: 12th IEEE International Conference on Intelligent Systems. Varna, Bulgaria 29 - 31 Aug 2024 IEEE. https://doi.org/10.1109/IS61756.2024.10705192
AI-Enhanced Prediction of Multi Organ Failure in COVID-19 Patients
Rajakaruna, I., Amirhosseini, M. H., Li, Y. and Arachcillage, D. J. 2024. AI-Enhanced Prediction of Multi Organ Failure in COVID-19 Patients. IS'24: 12th IEEE International Conference on Intelligent Systems. Varna, Bulgaria 29 - 31 Aug 2024 IEEE. https://doi.org/10.1109/IS61756.2024.10705181
Prediction of Depression Severity and Personalised Risk Factors Using Machine Learning on Multimodal Data
Amirhosseini, M. H., Ayodele, A. L. and Karami, A. 2024. Prediction of Depression Severity and Personalised Risk Factors Using Machine Learning on Multimodal Data. IS'24: 12th IEEE International Conference on Intelligent Systems. Varna, Bulgaria 29 - 31 Aug 2024 IEEE. https://doi.org/10.1109/IS61756.2024.10705185
An AI Powered System to Detect Autism Spectrum Disorder in Toddlers
Amirhosseini, M. H., Alam, N., Kalabi, F. and Virdee, B. 2024. An AI Powered System to Detect Autism Spectrum Disorder in Toddlers. ICDAM-2024: 5th International Conference on Data Analytics and Management. London, UK 14 - 15 Jun 2024
Machine Learning in Lithium-Ion Battery: Applications, Challenges, and Future Trends
Valizadeh, A. and Amirhosseini, M. 2024. Machine Learning in Lithium-Ion Battery: Applications, Challenges, and Future Trends. SN Computer Science. 5 (Art. 717). https://doi.org/10.1007/s42979-024-03046-2
A Graph-Based Method for Identity Resolution to Assist Police Force Investigative Process
Amirhosseini, M., Kazemian, H. and Phillips, M. 2024. A Graph-Based Method for Identity Resolution to Assist Police Force Investigative Process. Journal of Cyber Security and Technology. In Press. https://doi.org/10.1080/23742917.2024.2354555
Predictive precision in battery recycling: unveiling lithium battery recycling potential through machine learning
Valizadeh, A., Amirhosseini, M. H. and Ghorbani, Y. 2024. Predictive precision in battery recycling: unveiling lithium battery recycling potential through machine learning. Computers and Chemical Engineering. 183 (Art. 108623). https://doi.org/10.1016/j.compchemeng.2024.108623
An artificial intelligence approach to predicting personality types in dogs
Amirhosseini, M. H., Yadav, V., Serpell, J. A., Pettigrew, P. and Kain, P. 2024. An artificial intelligence approach to predicting personality types in dogs. Scientific Reports. 14 (Art. 2404). https://doi.org/10.1038/s41598-024-52920-9
Forecasting Bitcoin Prices in the Context of the COVID-19 Pandemic Using Machine Learning Approaches
Sontakke, P., Jafari, F., Saeedi, M. and Amirhosseini, M. 2024. Forecasting Bitcoin Prices in the Context of the COVID-19 Pandemic Using Machine Learning Approaches. ICDAM-2023: 4th International Conference on Data Analytics & Management. London, UK 23 - 24 Jun 2023 Springer. https://doi.org/10.1007/978-981-99-6544-1_7
An AI powered system to enhance self-reflection practice in coaching
Jelodari, M., Amirhosseini, M. H. and Giraldez Hayes, A. 2023. An AI powered system to enhance self-reflection practice in coaching. Cognitive Computation and Systems. 5 (4), pp. 243-254. https://doi.org/10.1049/ccs2.12087
Sentiment-Driven Cryptocurrency Price Prediction: A Machine Learning Approach Utilizing Historical Data and Social Media Sentiment Analysis
Bhatt, S., Ghazanfar, M. and Amirhosseini, M. 2023. Sentiment-Driven Cryptocurrency Price Prediction: A Machine Learning Approach Utilizing Historical Data and Social Media Sentiment Analysis. Machine Learning and Applications: An International Journal (MLAIJ). 10 (2/3), pp. 1-15. https://doi.org/10.5121/mlaij.2023.10301
Machine Learning based Cryptocurrency Price Prediction using historical data and Social Media Sentiment
Bhatt, S., Ghazanfar, M. and Amirhosseini, M. 2023. Machine Learning based Cryptocurrency Price Prediction using historical data and Social Media Sentiment . 5th International Conference on Machine Learning & Applications (CMLA 2023). Sydney, Australia 17 - 18 Jun 2023 AIRCC Publishing Corporation.
A Machine Learning Approach to Identify the Preferred Representational System of a Person
Amirhosseini, M. and Wall, J. 2022. A Machine Learning Approach to Identify the Preferred Representational System of a Person. Multimodal Technologies and Interaction. 6 (12), p. 112. https://doi.org/10.3390/mti6120112
Application of Graph-Based Technique to Identity Resolution
Kazemian, H., Amirhosseini, M. H. and Phillips, M. 2022. Application of Graph-Based Technique to Identity Resolution. AIAI 2022: 18th International Conference on Artificial Intelligence Applications and Innovations. Crete, Greece 17 - 20 Jun 2022 Springer. https://doi.org/10.1007/978-3-031-08333-4_38
A Rule and Graph-Based Approach for Targeted Identity Resolution on Policing Data
Phillips, M., Amirhosseini, M. and Kazemian, H. 2020. A Rule and Graph-Based Approach for Targeted Identity Resolution on Policing Data. 2020 IEEE Symposium Series on Computational Intelligence. Online 01 - 04 Dec 2020 IEEE. https://doi.org/10.1109/SSCI47803.2020.9308182
Machine Learning Approach to Personality Type Prediction Based on the Myers–Briggs Type Indicator®
Amirhosseini, M.H. and Kazemian, H. 2020. Machine Learning Approach to Personality Type Prediction Based on the Myers–Briggs Type Indicator®. Multimodal Technologies and Interaction. 4 (Art. 9). https://doi.org/10.3390/mti4010009
Automating the process of identifying the preferred representational system in Neuro Linguistic Programming using Natural Language Processing
Amirhosseini, M.H. and Kazemian, H. 2019. Automating the process of identifying the preferred representational system in Neuro Linguistic Programming using Natural Language Processing. Cognitive Processing. 20 (2), p. 175–193. https://doi.org/10.1007/s10339-019-00912-3
Natural Language Processing approach to NLP Meta model automation
Amirhosseini, M.H., Kazemian, H., Ouazzane, K. and Chandler, C. 2018. Natural Language Processing approach to NLP Meta model automation. 2018 International Joint Conference on Neural Networks (IJCNN). Rio de Janeiro, Brazil 08 - 13 Jul 2018 IEEE. https://doi.org/10.1109/IJCNN.2018.8489609