Cancer: Investigating the impact of the implementation platform on machine learning models

Book chapter


Olowolayemo, A. S., Souag, A. and Sirlantzis, K. 2024. Cancer: Investigating the impact of the implementation platform on machine learning models. in: Mengoni, M. and Souag, A. (ed.) AIHealth 2024, The First International Conference on AI-Health ThinkMind.
AuthorsOlowolayemo, A. S., Souag, A. and Sirlantzis, K.
EditorsMengoni, M. and Souag, A.
Abstract

In the context of global cancer prevalence and the imperative need to improve diagnostic efficiency, scientists have turned to machine learning (ML) techniques to expedite diagnosis processes. Although previous research has shown promising results in developing predictive models for faster cancer diagnosis, discrepancies in outcomes have emerged, even when employing the same dataset. This study addresses a critical question: does the choice of development platform for ML models impact their performance in cancer diagnosis? Utilizing the publicly available Wisconsin Diagnostic Breast Cancer (WDBC) dataset from the University of California, Irvine (UCI) to train four ML algorithms on two distinct platforms: Python SciKit-Learn and Knime Analytics. The algorithms’ performance was rigorously assessed and compared, with both platforms operating under their default configurations. The findings of this study underscore an impact of platform selection on ML model performance, emphasizing the need for thoughtful consideration when choosing a platform for predictive models’ development. Such a decision bears significant implications for model efficacy and, ultimately, patient outcomes in the healthcare industry. The source code (Python and Knime) and data for this study are made fully available through a public GitHub repository.

KeywordsCancer; Machine learning; Python SciKit-Learn; Knime analytics; Wisconsin Diagnostic Breast Cancer (WDBC)
Year2024
Book titleAIHealth 2024, The First International Conference on AI-Health
PublisherThinkMind
International Academy, Research, and Industry Association
Output statusPublished
File
License
All rights reserved
File Access Level
Open
ISBN9781685581367
Publication dates
Online10 Mar 2024
Publication process dates
Deposited11 Mar 2024
Official URLhttps://www.thinkmind.org/index.php?view=article&articleid=aihealth_2024_1_50_80026
Event AIHealth 2024, The First International Conference on AI-Health
Permalink -

https://repository.canterbury.ac.uk/item/9753x/cancer-investigating-the-impact-of-the-implementation-platform-on-machine-learning-models

Download files


File
aihealth_2024_1_50_80026.pdf
License: All rights reserved
File access level: Open

  • 192
    total views
  • 97
    total downloads
  • 5
    views this month
  • 1
    downloads this month

Export as

Related outputs

Utilising transformers for American Sign Language fingerspelling recognition
Pinnington, J., Souag, A. and Azhar, H. 2024. Utilising transformers for American Sign Language fingerspelling recognition. in: 24th International Symposium on Computational Intelligence and Informatics (CINTI 2024)
A novel dataset of annotated oyster mushroom images with environmental context for machine learning applications
Duman, S., Elewi, A., Hajhamed, A., Khankan, R., Souag, A. and Ahmed, A. 2024. A novel dataset of annotated oyster mushroom images with environmental context for machine learning applications. Data in Brief. https://doi.org/10.1016/j.dib.2024.111074
Light field imaging technology for virtual reality content creation: A review
Khan, A., Hossain, M., Covaci, A., Sirlantzis, K. and Xu, C. 2024. Light field imaging technology for virtual reality content creation: A review. IET Image Processing. 18 (11), pp. 2817-2837. https://doi.org/10.1049/ipr2.13144
Design and implementation of a cost-aware and smart oyster mushroom cultivation system
Souag, A., Elewi, A., Hajhamed, A., Khankan, R., Duman, S. and Ahmed, A. 2024. Design and implementation of a cost-aware and smart oyster mushroom cultivation system. Smart Agricultural Technology. Volume 8. https://doi.org/10.1016/j.atech.2024.100439
Effect of Gait Speed on Trajectory Prediction Using Deep Learning Models for Exoskeleton Applications
Kolaghassi, R., Marcelli, G. and Sirlantzis, K. 2023. Effect of Gait Speed on Trajectory Prediction Using Deep Learning Models for Exoskeleton Applications. Sensors. 23 (12), p. 5687. https://doi.org/10.3390/s23125687
Depth estimation and validation of plenoptic light field camera
Khan, Ali, Hossain, Md. Moinul, Sirlantzis, Konstantinos, Covaci, Alexandra and Chowdhury, Wasif Shafaet 2023. Depth estimation and validation of plenoptic light field camera. in: 2023 IEEE International Conference on Imaging Systems and Techniques (IST) IEEE.
Train vs. play: Evaluating the effects of gamified and non-gamified wheelchair skills training using virtual reality
Zorzi, C., Tabbaa, Luma, Covaci, Alexandra, Sirlantzis, Konstantinos and Marcelli, Gianluca 2023. Train vs. play: Evaluating the effects of gamified and non-gamified wheelchair skills training using virtual reality. Bioengineering. 10 (11), p. 1269. https://doi.org/10.3390/bioengineering10111269
Community areas of sustainable care and dementia excellence in Europe (CASCADE) - Final report: A process and technology evaluation of the CASCADE programme in the United Kingdom implementation sites
Martin, A., Hatzidimitriadou, E., Sangeorzan, I., Smith, R., Wright, T., Price, N., Sirlantzis, K. and Rajapakse, S. 2023. Community areas of sustainable care and dementia excellence in Europe (CASCADE) - Final report: A process and technology evaluation of the CASCADE programme in the United Kingdom implementation sites. Canterbury Canterbury Christ Church University.
Augmented reality (AR) for surgical robotic and autonomous systems: State of the art, challenges, and solutions
Seetohul, J,, Shafiee, M. and Sirlantzis, K. 2023. Augmented reality (AR) for surgical robotic and autonomous systems: State of the art, challenges, and solutions. Sensors. 23 (13), p. 6202. https://doi.org/10.3390/s23136202
Design and control of a single-leg exoskeleton with gravity compensation for children with unilateral cerebral palsy
Sarajchi, M. and Sirlantzis, K. 2023. Design and control of a single-leg exoskeleton with gravity compensation for children with unilateral cerebral palsy. Sensors. 23 (13), p. 6103. https://doi.org/1424-8220/23/13/6103
An annotated water-filled, and dry potholes dataset for deep learning applications
Dib, J., Sirlantzis, K. and Howells, G. 2023. An annotated water-filled, and dry potholes dataset for deep learning applications. Data in Brief. 48, p. 109206. https://doi.org/10.1016/j.dib.2023.109206
Why should everybody learn Artificial Intelligence?
Turner, S. and Souag, A. 2022. Why should everybody learn Artificial Intelligence? ETD blog, Canterbury Christ church University
Virtual and augmented reality in healthcare education (VARE) project report
Hatzidimitriadou, E., Taylor, J., Clark, N., Field, S., Reader, J., Buttery, A., Singh, B., Arealis, G. and Sirlantzis, K. 2022. Virtual and augmented reality in healthcare education (VARE) project report. Canterbury Canterbury Christ Church University.
How can the semantic web and ontologies help history and archeology
Souag, A. 2019. How can the semantic web and ontologies help history and archeology. in: Dans les dédales du web. Historiens en territoires numériques Paris Éditions de la Sorbonne.
Using the AMAN-DA method to generate security requirements: a case study in the maritime domain
Souag, A., Mazo, R., Salinesi, C. and Comyn-Wattiau, I. 2018. Using the AMAN-DA method to generate security requirements: a case study in the maritime domain. Requirements Engineering Journal. 23 (557–580). https://doi.org/10.1007/s00766-017-0279-5
Reusable knowledge in security requirements engineering: a systematic mapping study
Souag, A., Mazo, R., Salinesi, C. and Comyn-Wattiau, I. 2016. Reusable knowledge in security requirements engineering: a systematic mapping study. Requirements Engineering Journal. 21 (251–283). https://doi.org/10.1007/s00766-015-0220-8
A security ontology for security requirements elicitation
Souag, A. and Salinesi C., Mazo R., Comyn-Wattiau I. 2015. A security ontology for security requirements elicitation. https://doi.org/10.1007/978-3-319-15618-7_13
AMAN-DA: A knowledge reuse based approach for domain specific security requirements engineering
Souag, A. 2015. AMAN-DA: A knowledge reuse based approach for domain specific security requirements engineering. PhD Thesis Université Paris 1 Panthéon-Sorbonne CRI - Centre de Recherche en Informatique de Paris 1
A methodology for defining security requirements using security and domain ontologies
Souag, A., Salinesi C. and Comyn-Wattiau I. 2013. A methodology for defining security requirements using security and domain ontologies. Insight. Volume 16 (4), pp. 14-16. https://doi.org/10.1002/inst.201316414
Ontologies for security requirements: a literature survey and classification’
Souag, A. and Salinesi C., Comyn-Wattiau I. 2012. Ontologies for security requirements: a literature survey and classification’. https://doi.org/10.1007/978-3-642-31069-0_5