A Comparative Study on Machine Learning Algorithms for Predicting Breast Cancer Prognosis in Improving Clinical Trials

In recent years, machine learning algorithms have been more and more used in healthcare industry, especially in research areas involving human participants such as clinical trials and areas where the data is too expensive to collect. This research project has conducted a comparative study on three well-known machine learning methods: Logistic Regression (LR), Support Vector Machine (SVM), and Naïve Bayes (NB) against the same dataset for predicting breast cancer prognosis in improving clinical trials. The experiment results have provided a comprehensive view of the patient's risk levels and risk factors to clinicians that benefit in effective and efficient treatment. This research has also demonstrated that different machine learning algorithms against the same dataset for breast cancer prognosis can have a difference in both performance and accuracy. Therefore, the comparative study on different machine learning algorithms can identify the most suitable machine learning algorithm to achieve cost-effective clinical trials.