131,793 research outputs found
Blood tumor prediction using data mining techniques
Healthcare systems generate a huge data collected from medical tests. Data mining is the computing process of discovering patterns in large data sets such as medical examinations. Blood diseases are not an exception; there are many test data can be collected from their patients. In this paper, we applied data mining techniques to discover the relations between blood test characteristics and blood tumor in order to predict the disease in an early stage, which can be used to enhance the curing ability. We conducted experiments in our blood test dataset using three different data mining techniques which are association rules, rule induction and deep learning. The goal of our experiments is to generate models that can distinguish patients with normal blood disease from patients who have blood tumor. We evaluated our results using different metrics applied on real data collected from Gaza European hospital in Palestine. The final results showed that association rules could give us the relationship between blood test characteristics and blood tumor. Also, it demonstrated that deep learning classifiers has the best ability to predict tumor types of blood diseases with an accuracy of 79.45%. Also, rule induction gave us an explanation of rules that describes both tumor in blood and normal hematology
Quantitative Redundancy in Partial Implications
We survey the different properties of an intuitive notion of redundancy, as a
function of the precise semantics given to the notion of partial implication.
The final version of this survey will appear in the Proceedings of the Int.
Conf. Formal Concept Analysis, 2015.Comment: Int. Conf. Formal Concept Analysis, 201
Categorization of interestingness measures for knowledge extraction
Finding interesting association rules is an important and active research
field in data mining. The algorithms of the Apriori family are based on two
rule extraction measures, support and confidence. Although these two measures
have the virtue of being algorithmically fast, they generate a prohibitive
number of rules most of which are redundant and irrelevant. It is therefore
necessary to use further measures which filter uninteresting rules. Many
synthesis studies were then realized on the interestingness measures according
to several points of view. Different reported studies have been carried out to
identify "good" properties of rule extraction measures and these properties
have been assessed on 61 measures. The purpose of this paper is twofold. First
to extend the number of the measures and properties to be studied, in addition
to the formalization of the properties proposed in the literature. Second, in
the light of this formal study, to categorize the studied measures. This paper
leads then to identify categories of measures in order to help the users to
efficiently select an appropriate measure by choosing one or more measure(s)
during the knowledge extraction process. The properties evaluation on the 61
measures has enabled us to identify 7 classes of measures, classes that we
obtained using two different clustering techniques.Comment: 34 pages, 4 figure
- …