Chemometrics as an option to assess clinical data from diabetes mellitus type 2 patients

Nikolov, M.; Simeonova, P.; Simeonov, Vasil

doi:10.2478/s11536-009-0059-9

Cited by 3 publications

(2 citation statements)

References 14 publications

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“…The second class of pattern‐recognition methods aims at the detection of patterns that remain unnoticed in clinical medicine. A single pattern may become apparent when distinct patterns in a group differ from other patterns of the same category—they form a “cluster”; CA was used for multivariate statistical modeling of the input data .…”

Section: Introductionmentioning

confidence: 99%

Assessment and Modeling of Routinely Used Biochemical Laboratory Data of Healthy Individuals and End-Stage Renal Failure (ESRF) Patients by Three Different Chemometric Methods

Papaioannou

Rigas

Plageras

et al. 2013

J. Clin. Lab. Anal.

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Section: Introductionmentioning

confidence: 99%

Assessment and Modeling of Routinely Used Biochemical Laboratory Data of Healthy Individuals and End-Stage Renal Failure (ESRF) Patients by Three Different Chemometric Methods

Papaioannou

Rigas

Plageras

et al. 2013

J. Clin. Lab. Anal.

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“…Thus, the concept of a clinical decision support system is not new. There are many papers that describe these systems: some present reviews of working medical decision support software [4][5][6], whereas others are relate the problems involved in choosing the best method of classification for the particular medical task [7][8][9][10][11][12] or are cluster analyses based on a hierarchical dendrogram [13,14].…”

Section: Introductionmentioning

confidence: 99%

Different decision tree induction strategies for a medical decision problem

Burduk

Woźniak

2012

Open Medicine

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AbstractThe paper presents a comparative study of selected recognition methods for the medical decision problem -acute abdominal pain diagnosis. We consider if it is worth using expert knowledge and learning set at the same time. The article shows two groups of decision tree approaches to the problem under consideration. The first does not use expert knowledge and generates classifier only on the basis of learning set. The second approach utilizes expert knowledge for specifying the decision tree structure and learning set for determining mode of decision making in each node based on Bayes decision theory. All classifiers are evaluated on the basis of computer experiments.

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Chemometric assessment of clinical data for diabetes mellitus 2 type patients using self-organizing maps

Astel

Nikolov

Christov

et al. 2010

Journal of Environmental Science and Health, Part A

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The aim of this study was to offer an efficient and unsupervised strategy for medical data exploration to find relationships between clinical tests and major as well as concomitant syndromes of a specific disease. A large data set consisting of a group of 100 patients suffering diabetes mellitus type 2 disease characterized by more than 30 clinical parameters was explored using self-organizing maps (SOM) and classified by the use of non-hierarchical K-means algorithm implemented in the SOM. An attempt was made to correlate the classification results with the parameters of the metabolic syndrome. The classification result revealed 4 specific patterns of patients, each presented by respective discriminating parameters: (i) patients with well-controlled state of the disease, (ii) patients with disturbed kidney function, (iii) patients who do not keep the required medical treatment regime, and (iv) patients who neglect the disease. All patterns included respective number of patients with different clinical status and attitude to the health problem. Patients with a well-controlled state of the disease, although being chronic for a long period of time were characterized by very low density proteins and triglycerides levels as well as by the lowest levels for hemoglobin A(1c), total protein and creatine phosphokinase. Patients with disturbed kidney function were characterized by the highest averages of the uric acid, albumin, total protein and creatinine, and relatively low values for glucose. Patients who do not keep the required regime possessed the highest level of glucose along with maximal values for cholesterol and trombocytes. Patients who neglect the disease were characterized by high values of cholesterol and relatively high level of glucose, although the duration of the disease was the shortest as compared to the other groups of patients.

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Chemometrics as an option to assess clinical data from diabetes mellitus type 2 patients

Cited by 3 publications

References 14 publications

Assessment and Modeling of Routinely Used Biochemical Laboratory Data of Healthy Individuals and End-Stage Renal Failure (ESRF) Patients by Three Different Chemometric Methods

Assessment and Modeling of Routinely Used Biochemical Laboratory Data of Healthy Individuals and End-Stage Renal Failure (ESRF) Patients by Three Different Chemometric Methods

Different decision tree induction strategies for a medical decision problem

Chemometric assessment of clinical data for diabetes mellitus 2 type patients using self-organizing maps

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