A comparative assessment of machine learning models and algorithms for osteosarcoma cancer detection and classification
Osteosarcoma is a bone-forming tumor that is more common in children and young adults than in adults. Timely detection and classification of its type is crucial to its proper treatment and possible survival. Machine learning (ML) models trained on disease datasets are more effective in detection and...
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Elsevier
2025-06-01
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| Series: | Healthcare Analytics |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2772442524000820 |
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| author | Amoakoh Gyasi-Agyei |
| author_facet | Amoakoh Gyasi-Agyei |
| author_sort | Amoakoh Gyasi-Agyei |
| collection | DOAJ |
| description | Osteosarcoma is a bone-forming tumor that is more common in children and young adults than in adults. Timely detection and classification of its type is crucial to its proper treatment and possible survival. Machine learning (ML) models trained on disease datasets are more effective in detection and classification than the conventional methods with hand-crafted features highly dependent on pathologists’ expertise. A publicly available raw osteosarcoma dataset was explored and then preprocessed using different combinations of data denoising techniques (including principal component analysis, mutual information gain, analysis of variance and Kendall’s rank correlation analysis) and data augmentation to derive seven different datasets. Using the seven derived datasets and eight ML algorithms, this study designed and performed an extensive comparative analysis of seven sets of ML models (altogether over 160 models) with their hyperparameters optimized using grid search. The performance differences between the learned ML models were then validated using repeated stratified 10-fold cross-validation and 5x2 cross-validation paired t-tests to select the best model for our task. The empirical model based on the extra trees algorithm and fitted to class-balanced dataset via random oversampling and multicollinearity removed via principal component analysis proved to be the best, as it detected and classified osteosarcoma cancer in 10 ms with 97.8% area under the receiver operating characteristics curve and acceptably low false alarm and misdetection. Thus, the proposed models can be cutting-edge techniques for automated detection and classification of osteosarcoma tumors to aid timely diagnosis, prognosis, and treatment. |
| format | Article |
| id | doaj-art-0f8df580e2cd4c5b8101d937bf1cb7f3 |
| institution | Kabale University |
| issn | 2772-4425 |
| language | English |
| publishDate | 2025-06-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Healthcare Analytics |
| spelling | doaj-art-0f8df580e2cd4c5b8101d937bf1cb7f32025-01-09T06:15:00ZengElsevierHealthcare Analytics2772-44252025-06-017100380A comparative assessment of machine learning models and algorithms for osteosarcoma cancer detection and classificationAmoakoh Gyasi-Agyei0School of Information Technology & Engineering (SITE), Melbourne Institute of Technology (MIT), 288 La Trobe St, Melbourne VIC3000, AustraliaOsteosarcoma is a bone-forming tumor that is more common in children and young adults than in adults. Timely detection and classification of its type is crucial to its proper treatment and possible survival. Machine learning (ML) models trained on disease datasets are more effective in detection and classification than the conventional methods with hand-crafted features highly dependent on pathologists’ expertise. A publicly available raw osteosarcoma dataset was explored and then preprocessed using different combinations of data denoising techniques (including principal component analysis, mutual information gain, analysis of variance and Kendall’s rank correlation analysis) and data augmentation to derive seven different datasets. Using the seven derived datasets and eight ML algorithms, this study designed and performed an extensive comparative analysis of seven sets of ML models (altogether over 160 models) with their hyperparameters optimized using grid search. The performance differences between the learned ML models were then validated using repeated stratified 10-fold cross-validation and 5x2 cross-validation paired t-tests to select the best model for our task. The empirical model based on the extra trees algorithm and fitted to class-balanced dataset via random oversampling and multicollinearity removed via principal component analysis proved to be the best, as it detected and classified osteosarcoma cancer in 10 ms with 97.8% area under the receiver operating characteristics curve and acceptably low false alarm and misdetection. Thus, the proposed models can be cutting-edge techniques for automated detection and classification of osteosarcoma tumors to aid timely diagnosis, prognosis, and treatment.http://www.sciencedirect.com/science/article/pii/S2772442524000820Osteosarcoma classificationMachine learningAI in healthcareCancer detectionHealthcare informaticsMedical data mining |
| spellingShingle | Amoakoh Gyasi-Agyei A comparative assessment of machine learning models and algorithms for osteosarcoma cancer detection and classification Healthcare Analytics Osteosarcoma classification Machine learning AI in healthcare Cancer detection Healthcare informatics Medical data mining |
| title | A comparative assessment of machine learning models and algorithms for osteosarcoma cancer detection and classification |
| title_full | A comparative assessment of machine learning models and algorithms for osteosarcoma cancer detection and classification |
| title_fullStr | A comparative assessment of machine learning models and algorithms for osteosarcoma cancer detection and classification |
| title_full_unstemmed | A comparative assessment of machine learning models and algorithms for osteosarcoma cancer detection and classification |
| title_short | A comparative assessment of machine learning models and algorithms for osteosarcoma cancer detection and classification |
| title_sort | comparative assessment of machine learning models and algorithms for osteosarcoma cancer detection and classification |
| topic | Osteosarcoma classification Machine learning AI in healthcare Cancer detection Healthcare informatics Medical data mining |
| url | http://www.sciencedirect.com/science/article/pii/S2772442524000820 |
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