Decision trees to evaluate the risk of developing multiple sclerosis

Manuela Pasella; Fabio Pisano; Barbara Cannas; Alessandra Fanni; Eleonora Cocco; Jessica Frau; Francesco Lai; Stefano Mocci; Roberto Littera; Sabrina Rita Giglio

doi:10.3389/fninf.2023.1248632

Decision trees to evaluate the risk of developing multiple sclerosis

Front Neuroinform. 2023 Aug 15:17:1248632. doi: 10.3389/fninf.2023.1248632. eCollection 2023.

Authors

Manuela Pasella¹, Fabio Pisano¹, Barbara Cannas¹, Alessandra Fanni¹, Eleonora Cocco², Jessica Frau², Francesco Lai³, Stefano Mocci^#^{4

5}, Roberto Littera^#^{6

7}, Sabrina Rita Giglio^#^{4

5

6

7}

Affiliations

¹ Department of Electrical and Electronic Engineering, University of Cagliari, Cagliari, Italy.
² Department of Medical Science and Public Health, Centro Sclerosi Multipla, University of Cagliari, Cagliari, Italy.
³ Unit of Oncology and Molecular Pathology, Department of Biomedical Sciences, University of Cagliari, Cagliari, Italy.
⁴ Medical Genetics, Department of Medical Sciences and Public Health, University of Cagliari, Cagliari, Italy.
⁵ Centre for Research University Services, University of Cagliari, Monserrato, Italy.
⁶ AART-ODV (Association for the Advancement of Research on Transplantation), Cagliari, Italy.
⁷ Medical Genetics, R. Binaghi Hospital, ASSL Cagliari, ATS Sardegna, Cagliari, Italy.

^# Contributed equally.

Abstract

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.

Keywords: decision trees; human leukocyte antigen; immunogenetic risk markers; likelihood of multiple sclerosis development; machine learning; multiple classifier; multiple sclerosis.

Grants and funding

This research was supported by the “Fondazione di Sardegna,” grant no. 2023.0160 to the non-profit organization “Associazione per l’Avanzamento della Ricerca per i Trapianti (AART-ODV).”