Advanced Ensemble Classifier Techniques for Predicting Tumor Viability in Osteosarcoma Histological Slide Images


  • Tahsien Al-Quraishi Victorian Institute of Technology, School of IT, Melbourne, Victoria, Australia
  • Chee Keong NG Victorian Institute of Technology, School of IT, Melbourne, Victoria, Australia
  • Osama A. Mahdi Melbourne Institute of Technology, School of IT, Melbourne, Victoria, Australia
  • Amoakoh Gyasi Melbourne Institute of Technology, School of IT, Melbourne, Victoria, Australia
  • Naseer Al-Quraishi Alayen Iraqi University, College of Computer Science, Computer Science Department, Nasiriyah, Iraq



Osteosarcoma Cancer Histological, Slide Images Machine Learning, Feature Selection Ensemble Classifier, Voting Classifier, Multi-Layer Perceptron Random Forest, Logistic Regression AdaBoost Classifier, GB Classifier


Background: Osteosarcoma is considered as the primary malignant tumor of the bone, emanating from primitive mesenchymal cells that form osteoid or immature bone. Accurate diagnosis and classification play a key role in management planning to achieve improved patient outcomes. Machine learning techniques may be used to augment and surpass existing conventional methods towards an analysis of medical data.

Methods: In the present study, the combination of feature selection techniques and classification methods was used in the development of predictive models of osteosarcoma cases. The techniques include L1 Regularization (Lasso), Recursive Feature Elimination (RFE), SelectKBest, Tree-based Feature Importance, while the following classification methods were applied: Voting Classifier, Decision Tree, Naive Bayes, Multi-Layer Perceptron, Random Forest, Logistic Regression, AdaBoost, and Gradient Boosting. Some model assessment was done by combining metrics such as accuracy, precision, recall, F1 score, AUC, and V score.

Results: The combination of the Tree-Based Feature Importance for feature selection and Voting Classifier with Decision Tree Classifier proved to be giving a higher performance compared to all other combinations, where such combinations helped in correct classification of positive instances and wonderful minimization of false positives. Other combinations also gave significant performances but slightly less effective, for example, L1 Regularization with the Voting Classifier, RFE with the Voting Classifier.

Conclusion: This work presents strong evidence that advanced machine learning with ensemble classifiers and robust feature selection can result in overall improvement of the diagnostic accuracy and robustness for the classification of osteosarcoma. Research on class imbalance and computational efficiency will be its future research priority.


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How to Cite

Al-Quraishi , T., Keong NG , C., Mahdi , O. A., Gyasi, A., & Al-Quraishi, N. (2024). Advanced Ensemble Classifier Techniques for Predicting Tumor Viability in Osteosarcoma Histological Slide Images. Applied Data Science and Analysis, 2024, 52–68.
DOI: 10.58496/ADSA/2024/006
Published: 2024-05-29




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