Prediction of Early Recurrence of Liver Cancer by a Novel Discrete Bayes Decision Rule for Personalized Medicine.

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 the F1 measure compared to the existing scoring methods.