Application of machine learning for analyzing cancer patient data and predicting survival

Mitra, Diptarshi (2025) Application of machine learning for analyzing cancer patient data and predicting survival. International Journal of Science and Research Archive, 14 (1). pp. 949-953. ISSN 2582-8185

Cancer is a deadly disease, and a leading cause of death globally. Thus, the prediction of the possibility of survival of cancer patients, at an early stage of treatment, will be beneficial for both the doctors and the patients. This study has attempted to predict the survival status of cancer patients, by employing two well-known Machine Learning algorithms viz., Logistic Regression and Support Vector Machine, and utilizing a dataset of Kaggle. Before using the Machine Learning models, suitable encoding and scaling techniques have been applied on the data. However, neither of the Machine Learning algorithms has performed satisfactorily (accuracy of prediction for Logistic Regression: 51.6%, and that for Support Vector Machine: 52.2%), and the actual reason for this poor performance seems to be the low quality and/or the insufficiency of the data used.