1. Prediction of Early Recurrence of Liver Cancer by a Novel Discrete Bayes Decision Rule for Personalized Medicine
- Author
-
Hiroyuki Ogihara, Norio Iizuka, and Yoshihiko Hamamoto
- Subjects
0301 basic medicine ,Article Subject ,Early Recurrence ,lcsh:Medicine ,Machine learning ,computer.software_genre ,Sensitivity and Specificity ,General Biochemistry, Genetics and Molecular Biology ,03 medical and health sciences ,Humans ,Medicine ,Sensitivity (control systems) ,Precision Medicine ,Categorical variable ,Early Detection of Cancer ,Measure (data warehouse) ,General Immunology and Microbiology ,business.industry ,Liver Neoplasms ,lcsh:R ,Cancer ,Bayes Theorem ,General Medicine ,medicine.disease ,Test (assessment) ,030104 developmental biology ,Personalized medicine ,Artificial intelligence ,Neoplasm Recurrence, Local ,business ,Liver cancer ,computer ,Research Article - Abstract
We discuss a novel diagnostic method for predicting the early recurrence of liver cancer with high accuracy for personalized medicine. The difficulty with cancer treatment is that even if the types of cancer are the same, the cancers vary depending on the patient. Thus, remarkable attention has been paid to personalized medicine. Unfortunately, although the Tokyo Score, the Modified JIS, and the TNM classification have been proposed as liver scoring systems, none of these scoring systems have met the needs of clinical practice. In this paper, we convert continuous and discrete data to categorical data and keep the natively categorical data as is. Then, we propose a discrete Bayes decision rule that can deal with the categorical data. This may lead to its use with various types of laboratory data. Experimental results show that the proposed method produced a sensitivity of 0.86 and a specificity of 0.49 for the test samples. This suggests that our method may be superior to the well-known Tokyo Score, the Modified JIS, and the TNM classification in terms of sensitivity. Additional comparative study shows that if the numbers of test samples in two classes are the same, this method works well in terms of theF1measure compared to the existing scoring methods.
- Published
- 2016