AUTHOR=Gao Tianyu , Ren Hao , He Shan , Liang Deyi , Xu Yuming , Chen Kecheng , Wang Yufan , Zhu Yuxin , Dong Heling , Xu Zhongzhi , Chen Weiming , Cheng Weibin , Jing Fengshi , Tao Xiaoyu 

TITLE=Development of an interpretable machine learning-based intelligent system of exercise prescription for cardio-oncology preventive care: A study protocol

JOURNAL=Frontiers in Cardiovascular Medicine

VOLUME=Volume 9 - 2022

YEAR=2023

URL=https://www.frontiersin.org/journals/cardiovascular-medicine/articles/10.3389/fcvm.2022.1091885

DOI=10.3389/fcvm.2022.1091885

ISSN=2297-055X

ABSTRACT=Background: Cardiovascular disease (CVD) and cancer are the first and second causes of death in over 130 countries across the world. They are also among the top three causes in almost 180 countries worldwide. Cardiovascular complications are often noticed in cancer patients, with nearly 20% exhibiting cardiovascular comorbidities. Physical exercise may be useful in cancer survivors and people living with cancer (PLWC), as it prevents relapses, cardiovascular disease, and cardiotoxicity. Therefore, it is beneficial to recommend exercise as part of cardio-oncology preventive care.

Objective: With the progress of deep learning algorithms and the improvement of big data processing techniques, artificial intelligence (AI) has gradually become popular in the fields of medicine and healthcare. In the context of the shortage of medical resources in China, it is of great significance to adopt AI and machine learning methods for prescription recommendations. This study aims to develop an interpretable machine learning-based intelligent system of exercise prescription for cardio-oncology preventive care, and this paper presents the study protocol.

Methods: This will be a cohort study with retrospective machine learning modeling. We will recruit PLWC participants at baseline and follow up over several years. At baseline, doctors certificated by the American College of Sports Medicine will recommend exercise prescription to each participant. During the follow-up, effective exercise prescription will be determined by assessing the CVD status of participants.

Expected Outcomes: This study aims to develop not only an interpretable machine learning model to recommend exercise prescription but also an intelligent system of exercise prescription for precision cardio-oncology preventive care.

Ethics: This study is approved by the Research Ethics Committee, School of PE, Jinan University [JNUSPE-2022-08-01].

Clinical Trial Registration: This study is being registered with the Chinese Clinical Trials Registry. The registration number will be available in the next version of this manuscript.