SHS Web Conf.
Volume 139, 2022The 4th ETLTC International Conference on ICT Integration in Technical Education (ETLTC2022)
|Number of page(s)||6|
|Section||Topics in Computer Science|
|Published online||13 May 2022|
Random forest classification algorithm for medical industry data
1Department of Electrical and Computer Engineering, University of Western Macedonia, 501 00, Kozani, Greece
* Corresponding author: firstname.lastname@example.org
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.
© The Authors, published by EDP Sciences, 2022
This is an Open Access article distributed under the terms of the Creative Commons Attribution License 4.0, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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