# Machine Learning Predicts Outcomes of Phase III Clinical Trials for Prostate Cancer
## 4 Discussion
For both the three-study merged dataset models and the separate study validation models, the level of accuracy was similar for the three best performing models (for the three-study merged dataset models, these were 76% for XGB and voting classifier, and 75% with CatBoost). There were no statistically significant differences in accuracy between these models and thus their performance should be regarded as essentially equivalent. The performance of the more sophisticated ML algorithms (CatBoost, XGBoost, and Voting Classification) was superior to that of the simpler algorithms (logistic regression and K-nearest neighbors). This likely reflects that the data included complex non-linearities, which tend to favor the more sophisticated ML algorithms [29]. There are several strengths of this study. First, we were able to construct models with a level of accuracy which could be feasible for real-world systems for precision medicine. Second, our models' robustness was demonstrated both by the use of internal cross-validation for testing and also by the use of separate studies for model training and testing. Third, we used a range of modeling approaches, both in terms of algorithms used and the procedures used to select training and testing data. These variations allow an examination of how such factors affect model performance. Fourth, the data we used were typical, in terms of assessments and sample sizes, of Phase III clinical trials. These considerations point to the feasibility of using Phase III clinical trial data to develop tools for real-world clinical practice. Our study's most significant limitation is that it relied on a biomarker of disease progression (PSA levels) rather than direct measurements of drug efficacy, such as death or time to disease progression. A proxy for disease progression was necessary because there were insufficient data on death or disease progression for ML analysis. For this reason, the use of such biomarkers is common in clinical trials. A second limitation is that the measurement of some variables may have been suffered from biases between studies, for example, because the different studies were conducted in different countries. Such biases could have reduced the performance of the separate study validation models (where any such biases were not controlled) but do not pose a significant problem for the three-study merged dataset models (since the samples used for training and testing were shuffled) or the individual study models. Despite its limitations, this study represents an important step in demonstrating the feasibility of ML-based tools for predicting cancer treatment outcomes, with implications for precision oncology and clinical trial enrichment. Conceptualization, F.D.B. ; methodology, F.D.B., L.R.M.-P., S.G. and L.A.A. ; formal analysis, F.D.B., S.G. and L.A.A. ; data curation, F.D.B. and L.A.A. ; writing--original draft preparation, F.D.B. ; writing--review and editing, F.D.B. and L.R.M.-P.; visualization, F.D.B. and L.A.A. All authors have read and agreed to the published version of the manuscript. **Funding:** This research received no external funding. **Data Availability Statement:** Publicly available datasets were analyzed in this study. These datasets can be found in the following links: [[https://data.projectdatabase.org/projectdatabase/html/content/102](https://data.projectdatabase.org/projectdatabase/html/content/102)]([https://data.projectdatabase.org/projectdatabase/html/content/102](https://data.projectdatabase.org/projectdatabase/html/content/102)) (accessed on 21 October 2020).