Imputation and Hyperparameter Optimization in Cancer Diagnosis
by Yi Liu1 
, Wendy Wang2,* 
, Haibo Wang3
1 Department of Computer and Information Science, University of Massachusetts Dartmouth, MA 02747 USA
2 Department of Computer Science and Information Systems, University of North Alabama, Florence, AL 35632 USA
3 Division of International Business and Technology Studies, Texas A&M International University, TX 78041 USA
* Author to whom correspondence should be addressed.
Journal of Engineering Research and Sciences, Volume 2, Issue 8, Page # 1-18, 2023; DOI: 10.55708/js0208001
Keywords: Machine Learning, Cervical Cancer, Imputation, Hyperparameter Optimization
Received: 18 April 2023, Accepted: 17 August 2023, Published Online: 28 August 2023
APA Style
Chicago/Turabian Style
IEEE Style
Cancer is one of the leading causes for death worldwide. Accurate and timely detection of cancer can save lives. As more machine learning algorithms and approaches have been applied in cancer diagnosis, there has been a need to analyze their performance. This study has compared the detection accuracy and speed of nineteen machine learning algorithms using a cervical cancer dataset. To make the approach general enough to detect various types of cancers, this study has intentionally excluded feature selection, a feature commonly applied in most studies for a specific dataset or a certain type of cancer. In addition, imputation and hyperparameter optimization have been employed to improve the algorithms’ performance. The results suggest that when both imputation and hyperparameter optimization are applied, the algorithms tend to perform better than when either of them is employed individually or when both are absent. The majority of the algorithms have shown improved accuracy in diagnosis, although with the trade-off of increased execution time. The findings from this study demonstrate the potential of machine learning in cancer diagnosis, especially the possibility of developing versatile systems that are able to detect various types of cancers with satisfactory performance.
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