Random forest classification algorithm for medical industry data
Christodoulos Vlachas, Lazaros Damianos, Nikolaos Gousetis, Ioannis Mouratidis, Dimitrios Kelepouris, Konstantinos-Filippos Kollias, Nikolaos Asimopoulos, George F. Fragulis
Abstract
Medical industry produces a significant portion of data whereas by adopting various Machine Learning models it can make accurate predictions about public healthcare that can be generalised. Transfer learning improves traditional machine learning by transferring the knowledge learned in one or more tasks and by using it for learning improvement in a related target task. In the current study, transfer learning with random forests was applied. Four datasets of medical interest obtained from the University of California, Irvine (UCI) Machine Learning Repository were used i.e., the BUPA-Liver Disease Dataset, the Breast Cancer Wisconsin Dataset, the Cleveland Heart Disease Dataset, and the Pima Indians Diabetes dataset. To our knowledge, there has been no study that applied Random Forests and Transfer Learning for these datasets. According to our results, our proposed method could provide significant accuracy rates in terms of diagnosing these disorders. Specifically, the classification accuracy of each dataset was similar or higher compared to the majority of similar studies that applied Random Forests. Limitations and suggestions regarding future research are also presented.