RT info:eu-repo/semantics/article
T1 Systemic lupus erythematosus: how machine learning can help distinguish between infections and flares
A1 Usategui Martín, Iciar
A1 Arroyo, Yoel
A1 Torres Aranda, Ana María
A1 Barbado Ajo, María Julia
A1 Mateo, Jorge
K1 Systemic lupus erythematosus
K1 Lupus eritematoso
K1 Autoimmune diseases
K1 Enfermedades autoinmunes
K1 Medical treatment
K1 Tratamiento médico
K1 Medicine
K1 Inmunology
K1 Public health
K1 Machine learning
K1 Aprendizaje automático
K1 Artificial intelligence
K1 32 Ciencias Médicas
K1 2412 Inmunología
K1 3212 Salud Publica
K1 1203.04 Inteligencia Artificial
AB Systemic Lupus Erythematosus (SLE) is a multifaceted autoimmune ailment that impacts multiple bodily systems and manifests with varied clinical manifestations. Early detection is considered the most effective way to save patients’ lives, but detecting severe SLE activity in its early stages is proving to be a formidable challenge. Consequently, this work advocates the use of Machine Learning (ML) algorithms for the diagnosis of SLE flares in the context of infections. In the pursuit of this research, the Random Forest (RF) method has been employed due to its performance attributes. With RF, our objective is to uncover patterns within the patient data. Multiple ML techniques have been scrutinized within this investigation. The proposed system exhibited around a 7.49% enhancement in accuracy when compared to k-Nearest Neighbors (KNN) algorithm. In contrast, the Support Vector Machine (SVM), Binary Linear Discriminant Analysis (BLDA), Decision Trees (DT) and Linear Regression (LR) methods demonstrated inferior performance, with respective values around 81%, 78%, 84% and 69%. It is noteworthy that the proposed method displayed a superior area under the curve (AUC) and balanced accuracy (both around 94%) in comparison to other ML approaches. These outcomes underscore the feasibility of crafting an automated diagnostic support method for SLE patients grounded in ML systems.
PB MDPI
SN 2306-5354
YR 2024
FD 2024
LK https://uvadoc.uva.es/handle/10324/70050
UL https://uvadoc.uva.es/handle/10324/70050
LA eng
NO Bioengineering, 2024, Vol. 11, Nº. 1, 90
NO Producción Científica
DS UVaDOC
RD 08-ene-2025