Novel method for predicting nonvisible symptoms using machine learning in cancer palliative care.

ABSTRACT: End-of-life patients with cancer may find expressing their symptoms difficult if they can no longer communicate verbally because of deteriorating health. In this study, we assessed these symptoms using machine learning, which has excellent predictive capabilities and has recently been applied in healthcare. We performed a retrospective clinical survey involving 213 patients with cancer from August 2015 to August 2016. We divided the reported symptoms into two groups-visible and nonvisible symptoms. We used decision tree analysis, an analytical machine learning method that organizes and analyzes information in the form of a tree diagram to visually represent the information structure. Our machine learning model used patient background data and visible symptoms to predict nonvisible symptoms

SUBMITTER: Shimada K 

PROVIDER: S-EPMC10371999 | biostudies-literature | 2023 Jul

REPOSITORIES: biostudies-literature