A comparison of machine learning models for predicting urinary incontinence in men with localized prostate cancer.

ABSTRACT:

Introduction

Urinary incontinence (UI) is a common side effect of prostate cancer treatment, but in clinical practice, it is difficult to predict. Machine learning (ML) models have shown promising results in predicting outcomes, yet the lack of transparency in complex models known as "black-box" has made clinicians wary of relying on them in sensitive decisions. Therefore, finding a balance between accuracy and explainability is crucial for the implementation of ML models. The aim of this study was to employ three different ML classifiers to predict the probability of experiencing UI in men with localized prostate cancer 1-year and 2-year after treatment and compare their accuracy and explainability.

Methods

We used the ProZIB dataset from the Netherlands Comprehensive Cance

SUBMITTER: Hasannejadasl H 

PROVIDER: S-EPMC10130634 | biostudies-literature | 2023

REPOSITORIES: biostudies-literature