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The pair-wise linear classifier and the k-NN rule in application to ALS progression differentiation

Sokołowska B., Jóźwik A., Niebroj-Dobosz I., Janik P.

Journal of Medical Informatics & Technologies

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2012

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Vol. 20

79--83

EN

The two kinds of classifier based on the k-NN rule, the standard and the parallel version, were used for recognition of severity of ALS disease. In case of the second classifier version, feature selection was done separately for each pair of classes. The error rate, estimated by the leave one out method, was used as a criterion as for determination the optimum values of k's as well as for feature selection. All features selected in this manner were used in the standard and in the parallel classifier based on k-NN rule. Furthermore, only for the verification purpose, the linear classifier was applied. For this kind of classifier the error rates were calculated by use the training set also as a testing one. The linear classifier was trained by the error correction algorithm with a modified stop condition. The data set concerned with the healthy subjects and patients with amyotrophic lateral sclerosis (ALS). The set of several biomarkers such as erythropoietin, matrix metalloproteinases and their tissue inhibitors measured in serum and cerebrospinal fluid (CSF) were treated as features. It was shown that CSF biomarkers were very sensitive for the ALS progress.

2

Analysis of matrix metalloproteinases (MMPs) in cerebrospinal fluid of patients with amyotrophic lateral sclerosis (ALS)

Sokołowska B., Jóźwik A., Niebroj-Dobosz I., Janik P., Kwieciński H.

Journal of Medical Informatics & Technologies

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2009

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Vol. 13

147--150

EN

Matrix metalloproteinases (MMPs) are implicated in the pathogenesis of motor neuron degeneration in amyotrophic lateral sclerosis (ALS) and might be potential markers of diagnosis, prognosis and monitoring treatment effects. The aim of the present study was evaluation of the MMPs significance in cerebrospinal fluid (CSF MMPs) of patients with ALS in relation to severity of the disease. Metalloproteinases MT-MMP-1, MMP-2, MMP-9 and additionally age of subjects and disease duration were analyzed. The results demonstrate that the error of differentiation between healthy subjects and ALS patients (for MMP-2 feature) as well as between mild and severe ALS states (for CSF MMPs set) equalled to 0.033. In conclusion, the pattern recognition approach may be useful for differentation of ALS progressing on the basis of CSF MMPs features.

3

Pattern recognition approach to differentiation of disease severity in patients with amyotrophic lateral sclerosis

Jóźwik A., Sokołowska B., Niebroj-Dobosz I., Janik P., Kwieciński H.

Journal of Medical Informatics & Technologies

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2008

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Vol. 12

143--147

EN

A possibility of recognition of the clinical status of patients with amyotrophic lateral sclerosis (ALS) in relation to severity of the disease was investigated. Three groups: (i) healthy controls (n=15) and two subgroups of ALS patients (ii) mild (n=15) and (iii) severe (n=15) were considered as classes. Four features of the subjects: (i) their age (AGE) (ii) erythropoietin concentration in serum (SERUM), (iii) in cerebrospinal fluid (CSF), and (iv) duration time of the disease (Tdis) were used for classifier construction based on the k Nearest Neighbours (k-NN) rule, known from pattern recognition theory. The presented results demonstrate that the pattern recognition approach may be useful for the evaluation of the severity of the ALS disease.