Classification of Neurodegenerative Diseases using Machine Learning Methods

Aydın, Fatih; Aslan, Zafer

doi:10.18201/ijisae.2017526689

Cited by 12 publications

(11 citation statements)

References 23 publications

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“…However, the algorithm in [ 8 ] was implemented using all-train-all-test whereas our result is based on the validation set only. The algorithm in [ 12 ] used several features while our system only uses left-foot stride-to-stride interval. Moreover, our system can provide the shapes of prototypes that might be more understandable to user than the numeric algorithms.…”

Section: Experiments Resultsmentioning

confidence: 99%

“…Some research works involved detecting either PD or ALS only [ 9 , 13 , 14 ]. Some of them involved HD, ALS, and PD classification [ 8 , 10 – 12 ]; however, the information from left and right feet is used in the system. A few of them utilized only right-foot information to classify HD, ALS, and PD [ 7 ]; however, this method only detected a patient with one disease against a healthy patient, not finding a patient with one of the diseases against a healthy patient.…”

Section: Introductionmentioning

confidence: 99%

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String Grammar Unsupervised Possibilistic Fuzzy C-Medians for Gait Pattern Classification in Patients with Neurodegenerative Diseases

Klomsae

Auephanwiriyakul

Theera-Umpon

2018

Computational Intelligence and Neuroscience

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Neurodegenerative diseases that affect serious gait abnormalities include Parkinson's disease (PD), amyotrophic lateral sclerosis (ALS), and Huntington disease (HD). These diseases lead to gait rhythm distortion that can be determined by stride time interval of footfall contact times. In this paper, we present a new method for gait classification of neurodegenerative diseases. In particular, we utilize a symbolic aggregate approximation algorithm to convert left-foot stride-stride interval into a sequence of symbols using a symbolic aggregate approximation. We then find string prototypes of each class using the newly proposed string grammar unsupervised possibilistic fuzzy C-medians. Then in the testing process the fuzzy k-nearest neighbor is used. We implement the system on three 2-class problems, i.e., the classification of ALS against healthy patients, that of HD against healthy patients , and that of PD against healthy patients. The system is also implemented on one 4-class problem (the classification of ALS, HD, PD, and healthy patients altogether) called NDDs versus healthy. We found that our system yields a very good detection result. The average correct classification for ALS versus healthy is 96.88%, and that for HD versus healthy is 97.22%, whereas that for PD versus healthy is 96.43%. When the system is implemented on 4-class problem, the average accuracy is approximately 98.44%. It can provide prototypes of gait signals that are more understandable to human.

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Section: Experiments Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

String Grammar Unsupervised Possibilistic Fuzzy C-Medians for Gait Pattern Classification in Patients with Neurodegenerative Diseases

Klomsae

Auephanwiriyakul

Theera-Umpon

2018

Computational Intelligence and Neuroscience

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“…Nöro-dejeneratif hastalıkların (ALS, HD, PD) türünün erken belirlenmesi hastalığın ilerlemesini geciktirmek açısından önemlidir. [35]. Bu makalede diğer çalışmalardan farklı olarak özellik oluşturmak için tepe analizine dayalı tanımlayıcı veri özetleme yöntemi kullanıldı.…”

Section: İli̇şki̇li̇ çAlişmalar (Related Work)unclassified

Yapay Öğrenme Yöntemleri̇ Ve Dalgacik Dönüşümü Kullanilarak Nöro Dejenerati̇f Hastaliklarin Teşhi̇si̇

Aydın¹,

Aslan²

2017

Gazi Üniversitesi Mühendislik-Mimarlık Fakültesi Dergisi

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This study suggests that the force signals applied to the ground may be used to classify neuro-degenerative diseases (NDD) such as Amyotrophic lateral sclerosis (ALS), Huntington's disease (HD) and Parkinson's disease (PD). The experiments were performed using data with 16 control subjects (CO), 13 ALS, 20 HD and 15 PD. Firstly, the force signals were separated up to level-7 using Discrete Meyer (dmey) wavelet. Among the new signals, the approach signal at the seventh level was selected. The local maximums of the peaks, peak locations, peak widths and peak prominences were obtained by performing peak analysis on this signal. Then, 15 basic statistical features from each of these four peak features were obtained. Thus, 60 for each of left and right foot, 120 features were obtained. Among these 120 features, the ones giving the highest information were selected using OneRules classifier. Respectively, 93.1%, 97.22%, 83.87% and 92.18% accuracy was obtained on ALS-CO, HD-CO, PD-CO and NDD-CO datasets using Radial Basis Function Network (RBFNetwork), Adaptive Boosting (Adaboost) and Additive Logistic Regression (LogitBoost) algorithms.

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“…They used hill climbing method for selecting optimal feature subset and achieved accuracy of 96.83% in classifying healthy control and NDDs subjects. In a study by [16] classification of control and NDDs objects was performed using gait signals. For each right and left foot signals, 13 statistical features were computed.…”

Section: Introductionmentioning

confidence: 99%

Classification of Control and Neurodegenerative Disease Subjects Using Tree Based Classifiers

Shah

Habib

Aziz

et al. 2020

JPRI

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Background: The medical researchers are developing different non-invasive methods for early detection of Neurodegenerative Diseases (NDDs) when pharmacological interventions are still possible to further prevent the disease progression. The NDDs are associated with the degradation in the complex gait dynamics and motor activity. The classification of gait data using machine learning techniques can assist the physicians for early diagnosis of the neural disorder when clinical manifestation of the diseases is not yet apparent. Aims: The present study was undertaken to classify the control and NDD subjects using decision trees based classifiers (Random Forest (RF), J48 and REPTree). Methodology: The data used in the study comprises of 16 control, 20 Huntington’s Disease (HD), 15 Parkinson’s Disease (PD), and 13 Amyotrophic Lateral Sclerosis (ALS) subjects, which were taken from publicly available database from Physionet. The age range of control subjects was 20-74, HD subjects was 36-70, PD subjects was 44-80, and ALS subjects was 29-71. There were 13 attributes associated with the data. Important features/attributes of the data were selected using correlation feature selection - subset evaluation (cfs) method. Three tree based machine learning algorithms (RF, J48 and REPTree) were used to classify the control and NDD subjects. The performance of classifiers were evaluated using Precision, Recall, F-Measure, MAE and RMSE. Results: In order to evaluate the performance of tree based classifiers, two different settings of data i.e. complete features and selected features were used. In classifying control vs HD subjects, RF provides the robust separation with classification accuracy of 84.79% using complete features and 83.94% using selected features. While in classifying control vs PD subjects, and control vs ALS subjects, RF also provides the best separation with classification accuracy of 86.51% and 94.95% respectively using complete features and 85.19% and 93.64% respectively using selected features. Conclusion: The variability analysis of physiological signals provides a valuable non-invasive tool for quantifying the system of dynamics of healthy subjects and to examine the alternations in the controlling mechanism of these systems with aging and disease. It is concluded that selected features encode adequate information about neural control of the gait. Moreover, the selected features along with tree based machine learning algorithms can play a vital for early detection of NDDs, when pharmacological interventions are still possible.

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Classification of Neurodegenerative Diseases using Machine Learning Methods

Cited by 12 publications

References 23 publications

String Grammar Unsupervised Possibilistic Fuzzy C-Medians for Gait Pattern Classification in Patients with Neurodegenerative Diseases

String Grammar Unsupervised Possibilistic Fuzzy C-Medians for Gait Pattern Classification in Patients with Neurodegenerative Diseases

Yapay Öğrenme Yöntemleri̇ Ve Dalgacik Dönüşümü Kullanilarak Nöro Dejenerati̇f Hastaliklarin Teşhi̇si̇

Classification of Control and Neurodegenerative Disease Subjects Using Tree Based Classifiers

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