Predictive performance of machine learning compared to statistical methods in time-to-event analysis of cardiovascular disease: a systematic review protocol

Abubaker Suliman, Mohammad Masud, Mohamed Adel Serhani, Aminu S. Abdullahi, Abderrahim Oulhaj

Research output: Contribution to journalArticlepeer-review

Abstract

Background Globally, cardiovascular disease (CVD) remains the leading cause of death, warranting effective management and prevention measures. Risk prediction tools are indispensable for directing primary and secondary prevention strategies for CVD and are critical for estimating CVD risk. Machine learning (ML) methodologies have experienced significant advancements across numerous practical domains in recent years. Several ML and statistical models predicting CVD time-to-event outcomes have been developed. However, it is not known as to which of the two model types—ML and statistical models—have higher discrimination and calibration in this regard. Hence, this planned work aims to systematically review studies that compare ML with statistical methods in terms of their predictive abilities in the case of time-to-event data with censoring. Methods Original research articles published as prognostic prediction studies, which involved the development and/or validation of a prognostic model, within a peer-reviewed journal, using cohort or experimental design with at least a 12-month follow-up period will be systematically reviewed. The review process will adhere to the Critical Appraisal and Data Extraction for Systematic Reviews of Prediction Modelling Studies checklist. Ethics and dissemination Ethical approval is not required for this review, as it will exclusively use data from published studies. The findings of this study will be published in an open-access journal and disseminated at scientific conferences.

Original languageEnglish
Article numbere082654
JournalBMJ Open
Volume14
Issue number4
DOIs
Publication statusPublished - Apr 15 2024

ASJC Scopus subject areas

  • General Medicine

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