PMID- 37649987
OWN - NLM
STAT- PubMed-not-MEDLINE
LR  - 20230901
IS  - 1662-5196 (Print)
IS  - 1662-5196 (Electronic)
IS  - 1662-5196 (Linking)
VI  - 17
DP  - 2023
TI  - Decision trees to evaluate the risk of developing multiple sclerosis.
PG  - 1248632
LID - 10.3389/fninf.2023.1248632 [doi]
LID - 1248632
AB  - INTRODUCTION: Multiple sclerosis (MS) is a persistent neurological condition 
      impacting the central nervous system (CNS). The precise cause of multiple 
      sclerosis is still uncertain; however, it is thought to arise from a blend of 
      genetic and environmental factors. MS diagnosis includes assessing medical 
      history, conducting neurological exams, performing magnetic resonance imaging 
      (MRI) scans, and analyzing cerebrospinal fluid. While there is currently no cure 
      for MS, numerous treatments exist to address symptoms, decelerate disease 
      progression, and enhance the quality of life for individuals with MS. METHODS: 
      This paper introduces a novel machine learning (ML) algorithm utilizing decision 
      trees to address a key objective: creating a predictive tool for assessing the 
      likelihood of MS development. It achieves this by combining prevalent demographic 
      risk factors, specifically gender, with crucial immunogenetic risk markers, such 
      as the alleles responsible for human leukocyte antigen (HLA) class I molecules 
      and the killer immunoglobulin-like receptors (KIR) genes responsible for natural 
      killer lymphocyte receptors. RESULTS: The study included 619 healthy controls and 
      299 patients affected by MS, all of whom originated from Sardinia. The gender 
      feature has been disregarded due to its substantial bias in influencing the 
      classification outcomes. By solely considering immunogenetic risk markers, the 
      algorithm demonstrates an ability to accurately identify 73.24% of MS patients 
      and 66.07% of individuals without the disease. DISCUSSION: Given its notable 
      performance, this system has the potential to support clinicians in monitoring 
      the relatives of MS patients and identifying individuals who are at an increased 
      risk of developing the disease.
CI  - Copyright (c) 2023 Pasella, Pisano, Cannas, Fanni, Cocco, Frau, Lai, Mocci, Littera 
      and Giglio.
FAU - Pasella, Manuela
AU  - Pasella M
AD  - Department of Electrical and Electronic Engineering, University of Cagliari, 
      Cagliari, Italy.
FAU - Pisano, Fabio
AU  - Pisano F
AD  - Department of Electrical and Electronic Engineering, University of Cagliari, 
      Cagliari, Italy.
FAU - Cannas, Barbara
AU  - Cannas B
AD  - Department of Electrical and Electronic Engineering, University of Cagliari, 
      Cagliari, Italy.
FAU - Fanni, Alessandra
AU  - Fanni A
AD  - Department of Electrical and Electronic Engineering, University of Cagliari, 
      Cagliari, Italy.
FAU - Cocco, Eleonora
AU  - Cocco E
AD  - Department of Medical Science and Public Health, Centro Sclerosi Multipla, 
      University of Cagliari, Cagliari, Italy.
FAU - Frau, Jessica
AU  - Frau J
AD  - Department of Medical Science and Public Health, Centro Sclerosi Multipla, 
      University of Cagliari, Cagliari, Italy.
FAU - Lai, Francesco
AU  - Lai F
AD  - Unit of Oncology and Molecular Pathology, Department of Biomedical Sciences, 
      University of Cagliari, Cagliari, Italy.
FAU - Mocci, Stefano
AU  - Mocci S
AD  - Medical Genetics, Department of Medical Sciences and Public Health, University of 
      Cagliari, Cagliari, Italy.
AD  - Centre for Research University Services, University of Cagliari, Monserrato, 
      Italy.
FAU - Littera, Roberto
AU  - Littera R
AD  - AART-ODV (Association for the Advancement of Research on Transplantation), 
      Cagliari, Italy.
AD  - Medical Genetics, R. Binaghi Hospital, ASSL Cagliari, ATS Sardegna, Cagliari, 
      Italy.
FAU - Giglio, Sabrina Rita
AU  - Giglio SR
AD  - Medical Genetics, Department of Medical Sciences and Public Health, University of 
      Cagliari, Cagliari, Italy.
AD  - Centre for Research University Services, University of Cagliari, Monserrato, 
      Italy.
AD  - AART-ODV (Association for the Advancement of Research on Transplantation), 
      Cagliari, Italy.
AD  - Medical Genetics, R. Binaghi Hospital, ASSL Cagliari, ATS Sardegna, Cagliari, 
      Italy.
LA  - eng
PT  - Journal Article
DEP - 20230815
PL  - Switzerland
TA  - Front Neuroinform
JT  - Frontiers in neuroinformatics
JID - 101477957
PMC - PMC10465164
OTO - NOTNLM
OT  - decision trees
OT  - human leukocyte antigen
OT  - immunogenetic risk markers
OT  - likelihood of multiple sclerosis development
OT  - machine learning
OT  - multiple classifier
OT  - multiple sclerosis
COIS- The authors declare that the research was conducted in the absence of any 
      commercial or financial relationships that could be construed as a potential 
      conflict of interest. The reviewer MF declared a past co-authorship with the 
      author EC to the handling editor AF-C.
EDAT- 2023/08/31 06:41
MHDA- 2023/08/31 06:42
PMCR- 2023/01/01
CRDT- 2023/08/31 04:11
PHST- 2023/06/27 00:00 [received]
PHST- 2023/07/28 00:00 [accepted]
PHST- 2023/08/31 06:42 [medline]
PHST- 2023/08/31 06:41 [pubmed]
PHST- 2023/08/31 04:11 [entrez]
PHST- 2023/01/01 00:00 [pmc-release]
AID - 10.3389/fninf.2023.1248632 [doi]
PST - epublish
SO  - Front Neuroinform. 2023 Aug 15;17:1248632. doi: 10.3389/fninf.2023.1248632. 
      eCollection 2023.