Detection and severity classification of ataxia using gait features and a hybrid model

Srikantaswamy Pushpalatha, Vidyarani Hamppayanamaligae Jayaprakash, Sunitha Krishnamurthy

Abstract


Ataxia, a neurological disorder characterized by impaired coordination and unsteady movements, presents significant challenges for accurate diagnosis and classification. traditional machine-learning (ML) and deep-learning (DL) models often struggle to achieve high accuracy in predicting and classifying this complex condition. This study addresses these limitations by introducing a novel hybrid model, XGBoost-multi-layer-perceptron (XGB-MLP), specifically designed to enhance the accuracy of ataxia prediction and classification. The objective of this research is to develop a more reliable and precise diagnostic tool that outperforms existing ML and DL approaches. The methodology involved integrating the strengths of XGBoost, known for its powerful gradient boosting, with the multi-layer perceptron (MLP) neural network, creating a robust hybrid model. The proposed XGB-MLP model was rigorously tested against conventional models like random forest (RF), logistic regression (LR), support vector machine (SVM), MLP, and standalone XGBoost. The findings reveal that the XGB-MLP model achieves outstanding accuracy rates of 98.91% for ataxia prediction and 97.91% for classification, significantly surpassing the performance of the traditional models.

Keywords


Ataxia; Classification; Deep Learning; Machine Learning; Prediction

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DOI: http://doi.org/10.11591/ijeecs.v37.i1.pp560-568

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Indonesian Journal of Electrical Engineering and Computer Science (IJEECS)
p-ISSN: 2502-4752, e-ISSN: 2502-4760
This journal is published by the Institute of Advanced Engineering and Science (IAES) in collaboration with Intelektual Pustaka Media Utama (IPMU).

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