Quantitative Assessment of Ataxia in Multiple Sclerosis Patients using Spatiotemporal Parameters: A Relief-Based Machine Learning Analysis

Abstract

Background and Objective Multiple sclerosis (MS) is a chronic, progressive, and autoimmune disease of the central nervous system (CNS) characterized by inflammation, demyelination, and axonal injury. In patients with newly diagnosed MS (ndMS), ataxia can present either as mild or severe and can be difficult to diagnose in the absence of clinical disability. Such difficulties can be eliminated by using decision support systems supported by machine learning methods. The present study aimed to achieve early diagnosis of ataxia in ndMS patients by using machine learning methods with spatiotemporal parameters.

Materials and Methods The prospective study included 32 ndMS patients with an Expanded Disability Status Scale (EDSS) score of≤2.0 and 32 healthy volunteers. A total of 14 parameters were elicited by using a Win-Track platform. The ndMS patients were differentiated from healthy individuals using multiple classifiers including Artificial Neural Network (ANN), Support Vector Machine (SVM), the k-nearest neighbors (K-NN) algorithm, and Decision Tree Learning (DTL). To improve the performance of the classification, a Relief-based feature selection algorithm was applied to select the subset that best represented the whole dataset. Performance evaluation was achieved based on several criteria such as Accuracy (ACC), Sensitivity (SN), Specificity (SP), and Precision (PREC).

Results ANN had a higher classification performance compared to other classifiers, whereby it provided an accuracy, sensitivity, and specificity of 89, 87.8, 90.3% with the use of all parameters and provided the values of 93.7, 96.6%, and 91.1% with the use of parameters selected by the Relief algorithm, respectively.

Significance To our knowledge, this is the first study of its kind in the literature to investigate the diagnosis of ataxia in ndMS patients by using machine learning methods with spatiotemporal parameters. The proposed method, i. e. Relief-based ANN method, successfully diagnosed ataxia by using a lower number of parameters compared to the numbers of parameters reported in clinical studies, thereby reducing the costs and increasing the performance of the diagnosis. The method also provided higher rates of accuracy, sensitivity, and specificity in the diagnosis of ataxia in ndMS patients compared to other methods. Taken together, these findings indicate that the proposed method could be helpful in the diagnosis of ataxia in minimally impaired ndMS patients and could be a pathfinder for future studies.

Zusammenfassung

Hintergrund und Ziel Multiple Sklerose (MS) ist eine chronische, fortschreitende und Autoimmunerkrankung des Zentralnervensystems (ZNS), die durch Entzündung, Demyelinisierung und axonale Verletzung gekennzeichnet ist. Bei Patienten mit neu diagnostizierter MS (ndMS) kann Ataxie entweder leicht oder schwer auftreten und ohne klinische Behinderung schwierig zu diagnostizieren sein. Solche Schwierigkeiten können durch die Verwendung von Entscheidungsunterstützungssystemen beseitigt werden, die durch Methoden des maschinellen Lernens unterstützt werden. Die vorliegende Studie zielte darauf ab, eine frühzeitige Diagnose der Ataxie zu erreichen bei ndMS-Patienten mithilfe von Methoden des maschinellen Lernens mit raumzeitlichen Parametern zu bewerten.

Material und Methoden Die prospektive Studie umfasste 32 ndMS-Patienten mit einem EDSS-Wert (Expanded Disability Status Scale) von≤2,0 und 32 gesunde Freiwillige. Insgesamt 14 Parameter wurden mithilfe einer Win-Track-Plattform ermittelt. Die ndMS-Patienten wurden von gesunden Personen unter Verwendung mehrerer Klassifikatoren unterschieden, einschließlich des künstlichen neuronalen Netzwerks (KNN), der Support Vector Machine (SVM), des Algorithmus für k-nächste Nachbarn (K-NN) und des Decision Tree Learning (DTL). Um die Leistung der Klassifizierung zu verbessern, wurde ein auf Relief basierender Algorithmus zur Merkmalsauswahl angewendet, um die Teilmenge auszuwählen, die den gesamten Datensatz am besten repräsentiert. Die Leistungsbewertung wurde anhand verschiedener Kriterien wie Regelgenauigkeit, Empfindlichkeit, Spezifität und Präzision erreicht.

Ergebnisse ANN hatte im Vergleich zu anderen Klassifikatoren eine höhere Klassifizierungsleistung, wobei es bei Verwendung aller Parameter eine Genauigkeit, Sensitivität und Spezifität von 89, 87,8, 90,3% und Werte von 93,7, 96,6% und 91,1 ergab% unter Verwendung von Parametern, die vom Relief-Algorithmus ausgewählt wurden.

Schlussfolgerun Nach unserem Kenntnisstand ist dies die erste Studie dieser Art in der Literatur, die die Diagnose von Ataxie bei ndMS-Patienten mithilfe maschineller Lernmethoden mit raumzeitlichen Parametern untersucht. Das vorgeschlagene Verfahren, d. H. Das entlastungsbasierte ANN-Verfahren, diagnostizierte erfolgreich Ataxie unter Verwendung einer geringeren Anzahl von Parametern im Vergleich zu der Anzahl von Parametern, die in klinischen Studien angegeben wurden, wodurch die Kosten gesenkt und die

Key words

Multiple sclerosis - Ataxia - Machine learning - Relief method - Artificial neural networks

Schlüsselwörter

Multiple Sklerose - Entlastungsmethode - Künstliche Neuronale Netze - Ataxie - maschinelles Lernen

Publikationsverlauf

Eingereicht: 28. Dezember 2020

Angenommen: 19. Mai 2021

Artikel online veröffentlicht:
23. August 2021

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