Multiple Sclerosis Severity Estimation and Progression Prediction Based on Machine Learning Techniques

Daphni Plati; Evanthia Tripoliti; Styliani Zelilidou; Kostas Vlachos; Spyridon Konitsiotis; Dimitrios I Fotiadis

doi:10.1109/EMBC48229.2022.9871213

Multiple Sclerosis Severity Estimation and Progression Prediction Based on Machine Learning Techniques

Annu Int Conf IEEE Eng Med Biol Soc. 2022 Jul:2022:1109-1112. doi: 10.1109/EMBC48229.2022.9871213.

Authors

Daphni Plati, Evanthia Tripoliti, Styliani Zelilidou, Kostas Vlachos, Spyridon Konitsiotis, Dimitrios I Fotiadis

PMID: 36085783
DOI: 10.1109/EMBC48229.2022.9871213

Abstract

The aim of the study is to address the Multiple Sclerosis (MS) severity estimation problem based on EDSS score and the prediction of the disease's progression with the application of Machine Learning (ML) approaches. Several ML techniques are implemented. The data are provided by the Neurology Clinic of the University Hospital of Ioannina and were collected in the framework of the ProMiSi project. The features recorded are grouped into: general demographic information, MS clinical related data, results of special tests, treatment, and comorbidities. The records from 30 patients are utilized and are recorded in three time points. The ML methods provided quite high results with 94.87% accuracy for the MS severity estimation and 83.33% for the disease's progression prediction.

Publication types

Research Support, Non-U.S. Gov't

MeSH terms

Ambulatory Care Facilities
Humans
Machine Learning
Multiple Sclerosis* / diagnosis