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Survival trees are a non-parametric method used in survival analysis to model the relationship between a set of covariates and the time until an event of interest occurs, often referred to as the "time-to-event" or "survival time." This method is particularly useful when dealing with censored data, where the event has not occurred for some individuals by the end of the study period, or when the exact time of the event is unknown.
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A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment

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Published on: January 11, 2020

Decision trees to evaluate the risk of developing multiple sclerosis.

Manuela Pasella¹, Fabio Pisano¹, Barbara Cannas¹

¹Department of Electrical and Electronic Engineering, University of Cagliari, Cagliari, Italy.

Frontiers in Neuroinformatics

|August 31, 2023

Summary

A new machine learning model uses immunogenetic markers to predict multiple sclerosis (MS) risk. This tool can help identify at-risk individuals and monitor family members of MS patients.

Keywords:

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

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Area of Science:

Neurology
Immunogenetics
Machine Learning

Background:

Multiple sclerosis (MS) is a chronic neurological disease affecting the central nervous system, with uncertain etiology attributed to genetic and environmental factors.
Current MS diagnosis involves clinical assessment, neuroimaging, and cerebrospinal fluid analysis, with no definitive cure available.

Purpose of the Study:

To develop a predictive machine learning tool for assessing the risk of developing multiple sclerosis.
To identify key immunogenetic risk markers associated with MS development.

Main Methods:

A decision tree-based machine learning algorithm was developed.
The algorithm integrated demographic factors (initially) and immunogenetic markers, including Human Leukocyte Antigen (HLA) class I alleles and Killer Immunoglobulin-like Receptor (KIR) genes.
Demographic factors were excluded due to bias, focusing solely on immunogenetic markers.

Main Results:

The study included 619 healthy controls and 299 MS patients from Sardinia.
Excluding gender, the algorithm achieved 73.24% accuracy in identifying MS patients and 66.07% in identifying healthy individuals.
The model demonstrated the predictive power of immunogenetic markers in MS risk assessment.

Conclusions:

The developed machine learning system shows potential for clinical application.
It can aid in monitoring relatives of MS patients and identifying at-risk individuals.
Further research may refine predictive capabilities for early MS detection and intervention.