Fair cardiovascular disease diagnosis and prognosis through machine learning and fractal-based features

Entity: Universitat Rovira i Virgili (URV)

Confidenciality: No

Education area(s): Ciència de Dades Biomèdiques

APS: No

Title in different languages: Fair cardiovascular disease diagnosis and prognosis through machine learning and fractal-based features

Abstract: Given the high number of deaths caused by cardiovascular diseases, innovative methods are essential to mitigate their detrimental effects. Fractal analysis offers a detailed representation of complex patterns, like those in cardiovascular conditions. This thesis evaluates the fairness and predictive performance of ML models using fractal features from CMR for diagnosing and prognosing cardiovascular diseases. No significant differences were found between the best fractal and radiomics models. After mitigation, fractals were superior to radiomics. Considering their equivalent predictive performance, reduced bias after mitigation, and fewer features, fractals are proposed as an alternative to radiomics.

Subject: Sistema cardiovascular--Malalties

Academic year: 2023-2024

Language: en

Work's public defense date: 2024-06-20

Subject areas: Computer engineering

Student: Colmenero Gomez Cambronero, Carlos

Department: Enginyeria Informàtica i Matemàtiques

Creation date in repository: 2025-03-03

Keywords: Machine learning, fractals, cardiovascular diseases, diagnosis, prognosis, fair models

Title in original language: Fair cardiovascular disease diagnosis and prognosis through machine learning and fractal-based features

Access Rights: info:eu-repo/semantics/openAccess

Project director: Gkontra, Polyxeni