Citation

Muthaiyah, Saravanan and Singh, Vivek Ajit and Zaw, Thein Oak Kyaw and Sonai Muthu Anbananthen, Kalaiarasi and Park, Byeonghwa and Kim, Myung Joon (2023) A Binary Survivability Prediction Classification Model towards Understanding of Osteosarcoma Prognosis. Emerging Science Journal, 7 (4). pp. 1294-1314. ISSN 2610-9182

Text 1890-5506-1-PB.pdf - Published Version Restricted to Repository staff only Download (1MB)

Abstract

The objective of this study is to explore effective and innovative machine learning techniques that can assist medical professionals in developing more accurate prognoses that can enhance the survivability of osteosarcoma patients by investigating potential prognostic factors and identifying novel therapeutic approaches. A comprehensive analysis was conducted using a dataset of 128 osteosarcoma patients between 1997 to 2011. The dataset included 52 attributes in total that covered a wide range of demographics, together with information on clinical records, treatment protocols, and survival outcomes. Data was obtained from NOCERAL (National Orthopaedic Centre of Excellence in Research and Learning), Kuala Lumpur. Three distinct binary classification methods (i.e., random forest, support vector machine (SVM), and artificial neural network (ANN)) were employed to identify the prognostic factors that are associated with improved survival efficacy measures. The results of this study revealed that both SVM and ANN outperformed random forests in predicting survivability for both the 2-year and 5-year time frames. These findings indicate the potential of SVM and ANN as effective tools for predicting osteosarcoma survivability. The study signifies a significant step towards integrating machine learning techniques into the existing toolkit available to medical practitioners. This study contributes to the medical field by providing a comparative analysis of three prominent machine learning techniques for predicting osteosarcoma survivability. The superior performance of SVM and ANN over random forests highlights the potential of these methods in generating more accurate survivability predictions. Further development and refinement of these machine learning techniques hold promise for enhancing their effectiveness and instilling greater confidence among medical professionals and patients in the predictive capabilities of machine learning and artificial intelligence models for osteosarcoma survivability.

Item Type:	Article
Uncontrolled Keywords:	Osteosarcoma; Survivability; Prognosis; Machine Learning.
Subjects:	Q Science > Q Science (General) > Q300-390 Cybernetics
Divisions:	Faculty of Management (FOM)
Depositing User:	Ms Nurul Iqtiani Ahmad
Date Deposited:	04 Sep 2023 01:59
Last Modified:	04 Sep 2023 01:59
URII:	http://shdl.mmu.edu.my/id/eprint/11639

Downloads

Edit (login required)