Disease modelling using evolved discriminate function

Precocious diagnosis increases the survival time and patient quality of life. It is a binary classification, exhaustively studied in the literature. This paper innovates proposing the application of genetic programming to obtain a discriminate function. This function contains the disease dynamics used to classify the patients with as little false negative diagnosis as possible. If its value is greater than zero then it means that the patient is ill, otherwise healthy. A graphical representation is proposed to show the influence of each dataset attribute in the discriminate function. The experiment deals with Breast Cancer and Thrombosis & Collagen diseases diagnosis. The main conclusion is that the discriminate function is able to classify the patient using numerical clinical data, and the graphical representation displays patterns that allow understanding of the model

Disease modeling using Evolved Discriminate Function

Precocious diagnosis increases the survival time and patient quality of life. It is a binary classification, exhaustively studied in the literature. This paper innovates proposing the application of genetic programming to obtain a discriminate function. This function contains the disease dynamics used to classify the patients with as little false negative diagnosis as possible. If its value is greater than zero then it means that the patient is ill, otherwise healthy. A graphical representation is proposed to show the influence of each dataset attribute in the discriminate function. The experiment deals with Breast Cancer and Thrombosis & Collagen diseases diagnosis. The main conclusion is that the discriminate function is able to classify the patient using numerical clinical data, and the graphical representation displays patterns that allow understanding of the model

Some notes on an extended query language for FSM

FSM is a database model that has been recently proposed by the authors. FSM uses basic concepts of classification, generalization, aggregation and association that are commonly used in semantic modelling and supports the fuzziness of real-world at attribute, entity, class and relations intra and inter-classes levels. Hence, it provides tools to formalize and conceptualize real-world within a manner adapted to human perception of and reasoning about this real-word. In this paper we briefly review basic concepts of FSM and provide some notes on an extended query language adapted to it.ou

Minimal Decision Rules Based on the A Priori Algorithm

Based on rough set theory many algorithms for rules extraction from data have been proposed. Decision rules can be obtained directly from a database. Some condition values may be unnecessary in a decision rule produced directly from the database. Such values can then be eliminated to create a more comprehensi- ble (minimal) rule. Most of the algorithms that have been proposed to calculate minimal rules are based on rough set theory or machine learning. In our ap- proach, in a post-processing stage, we apply the Apriori algorithm to reduce the decision rules obtained through rough sets. The set of dependencies thus obtained will help us discover irrelevant attribute values

Do Parties Matter for Fiscal Policy Choices? A Regression-Discontinuity Approach

This paper presents a method for measuring the causal effect of party control on fiscal policy outcomes. The source of identifying information comes from an institutional feature of the election system, namely that party control changes discontinuously at 50 percent of the vote share, i.e., a party that receives more than 50 percent of the votes will be in office. The approach is applied to a very large panel data set from Swedish local governments, which offers a number of attractive features. The results show that there is large and significant party effect: on average, left-wing parties spend and tax 2.5 percent more than right-wing governments. The party effect constitutes 1 percent of average municipality income, clearly a sizeable effect.political parties; party control; regression-discontinuity design

Diffuse pattern learning with Fuzzy ARTMAP and PASS

Fuzzy ARTMAP is compared to a classifier system (CS) called PASS (predictive adaptive sequential system). Previously reported results in a benchmark classification task suggest that Fuzzy ARTMAP systems perform better and are more parsimonious than systems based on the CS architecture. The tasks considered here differ from ordinary classificatory tasks in the amount of output uncertainty associated with input categories. To be successful, learning systems must identify not only correct input categories, but also the most likely outputs for those categories. Performance under various types of diffuse patterns is investigated using a simulated scenario

A first approach to the multipurpose relational database server

In this paper, an architecture and an implementation of a multipurpose relational database server are proposed. This architecture enables classical queries to be executed, deductions to be made, and data mining operations to be performed on fuzzy or classical data. The proposal of this integration is to combine several ways of querying different types of data. In order to achieve this, a combination of existing metaknowledge bases and new data catalog elements is presented. We also introduce a language for handling all these data coherently and uniformly on the basis of classical SQL sentences