scholarly journals CLASSIFICATION OF PATIENT ADMISSIONS ACCORDING TO MAJOR DIAGNOSTIC CLASSIFICATION (MDC) USING VISUAL DATA MINING METHODS

2015 ◽  
Vol 5 (15) ◽  
pp. 54-54
Author(s):  
Keziban AVCI ◽  
Songül ÇINAROĞLU
Keyword(s):  
Data Mining ◽  
Diagnostic Classification ◽  
Visual Data ◽  
Visual Data Mining ◽  
Patient Admissions ◽  
Mining Methods

Visual Data Mining in Physiotherapy Using Self-Organizing Maps

2012 ◽  
pp. 187-194 ◽  
Author(s):  
Yasser Alakhdar ◽  
José M. Martínez-Martínez ◽  
Josep Guimerà-Tomás ◽  
Pablo Escandell-Montero ◽  
Josep Benitez ◽  
...  
Keyword(s):  
Data Mining ◽  
Cruciate Ligament ◽  
Visual Data ◽  
Visual Data Mining ◽  
Process Data ◽  
Self Organizing Maps ◽  
Clinical Specialist ◽  
Post Surgery ◽  
Mining Methods ◽  
Anterior Cruciate

The basis of all clinical science developments is the analysis of the data obtained from a particular problem. In recent decades, however, the capacity of computers to process data has been increasing exponentially, which has created the possibility of applying more powerful methods of data analysis. Among these methods, the multidimensional visual data mining methods are outstanding. These methods show all the variables of one particular problem on the whole allowing to the clinical specialist to extract his own conclusions. In this chapter, a neural approximation to this kind of data mining is shown by means of the valuation analysis of the knee in athletes in the pre- and post-surgery of the anterior cruciate ligament, studying variables of force and measurements at different distances of the knee.

Visual Data Mining in Physiotherapy Using Self-Organizing Maps

Data Mining ◽  
2013 ◽  
pp. 650-657 ◽  
Author(s):  
Yasser Alakhdar ◽  
José M. Martínez-Martínez ◽  
Josep Guimerà-Tomás ◽  
Pablo Escandell-Montero ◽  
Josep Benitez ◽  
...  
Keyword(s):  
Data Mining ◽  
Cruciate Ligament ◽  
Visual Data ◽  
Visual Data Mining ◽  
Process Data ◽  
Self Organizing Maps ◽  
Clinical Specialist ◽  
Post Surgery ◽  
Mining Methods ◽  
Anterior Cruciate

The basis of all clinical science developments is the analysis of the data obtained from a particular problem. In recent decades, however, the capacity of computers to process data has been increasing exponentially, which has created the possibility of applying more powerful methods of data analysis. Among these methods, the multidimensional visual data mining methods are outstanding. These methods show all the variables of one particular problem on the whole allowing to the clinical specialist to extract his own conclusions. In this chapter, a neural approximation to this kind of data mining is shown by means of the valuation analysis of the knee in athletes in the pre- and post-surgery of the anterior cruciate ligament, studying variables of force and measurements at different distances of the knee.

A data mining approach to the diagnosis of failure modes for two serial fastened sandwich composite plates

2016 ◽  
Vol 51 (20) ◽  
pp. 2853-2862 ◽  
Author(s):  
Serkan Ballı
Keyword(s):  
Data Mining ◽  
Random Forest ◽  
Failure Modes ◽  
Composite Plates ◽  
Study Data ◽  
Sandwich Composite ◽  
Support Vector ◽  
Geometrical Parameters ◽  
Mining Methods

The aim of this study is to diagnose and classify the failure modes for two serial fastened sandwich composite plates using data mining techniques. The composite material used in the study was manufactured using glass fiber reinforced layer and aluminum sheets. Obtained results of previous experimental study for sandwich composite plates, which were mechanically fastened with two serial pins or bolts were used for classification of failure modes. Furthermore, experimental data from previous study consists of different geometrical parameters for various applied preload moments as 0 (pinned), 2, 3, 4, and 5 Nm (bolted). In this study, data mining methods were applied by using these geometrical parameters and pinned/bolted joint configurations. Therefore, three geometrical parameters and 100 test data were used for classification by utilizing support vector machine, Naive Bayes, K-Nearest Neighbors, Logistic Regression, and Random Forest methods. According to experiments, Random Forest method achieved better results than others and it was appropriate for diagnosing and classification of the failure modes. Performances of all data mining methods used were discussed in terms of accuracy and error ratios.

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