Evaluating the impact of machine learning platforms on cancer classification model performance: A cross-platform comparative study
Journal article
Olowolayemo, A., Souag, A. and Sirlantzis, K. 2024. Evaluating the impact of machine learning platforms on cancer classification model performance: A cross-platform comparative study. International Journal on Advanced in Life Sciences. 16 (no 3 & 4), pp. 96 - 111.
Authors | Olowolayemo, A., Souag, A. and Sirlantzis, K. |
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Abstract | Machine Learning techniques have become pivotal in advancing predictive models for early cancer detection, addressing the growing need for improved diagnostic efficiency. However, the role of implementation platforms in influencing model performance remains underexplored, even as variationsin performance with the same dataset raise questions about platform choice. This study evaluates the impact of three ML implementation tools, the Scikit-learn, KNIME, and MATLAB on the performance of four classification algorithms: Logistic Regression, Decision Tree, Random Forest, and Gradient Boosting. Using the publicly available Wisconsin Diagnostic Breast Cancer dataset, these algorithms were implemented under default configurations and compared across key metrics: accuracy, recall, precision, and F1-score. Results revealed significant platform-dependent variations: Scikit-learn achieved consistently higher recall, particularly for Random Forest and Gradient Boosting, making it more effective at minimising false negatives critical in cancer diagnosis. MATLAB demonstrated superior precision, especially for Random Forest and Gradient Boosting, indicating potential in reducing false positives. KNIME, while effective in specific contexts, underperformed in recall and precision, raising concerns in scenarios requiring high sensitivity and specificity. These findings underscore the importance of platform selection based on predictive task requirements, especially in healthcare, where balancing false positives and false negatives is crucial. The study provides actionable insights for selecting ML platforms to enhance diagnostic accuracy in cancer classification tasks, with source code and data fully accessible through a public GitHub repository. |
Keywords | Cancer ; Machine learning; Python Scikit-learn; KNIME; MATLAB; Wisconsin Diagnostic Breast Cancer |
Year | 2024 |
Journal | International Journal on Advanced in Life Sciences |
Journal citation | 16 (no 3 & 4), pp. 96 - 111 |
Publisher | IARIA |
ISSN | 1942-2660 |
Official URL | https://www.iariajournals.org/life_sciences/tocv16n34.html |
https://www.iariajournals.org/life_sciences/lifsci_v16_n34_2024_paged.pdf | |
Publication process dates | |
Deposited | 23 Jan 2025 |
Accepted author manuscript | License File Access Level Open |
Output status | Published |
https://repository.canterbury.ac.uk/item/9q224/evaluating-the-impact-of-machine-learning-platforms-on-cancer-classification-model-performance-a-cross-platform-comparative-study
Download files
Accepted author manuscript
IARIA_Camera Ready...Evaluating the Impact of Machine Learning Platforms on Cancer Classification Model Performance_A Cros.pdf | ||
License: CC BY-NC-ND 4.0 | ||
File access level: Open |
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