60,704 research outputs found
Decision support methods in diabetic patient management by insulin administration neural network vs. induction methods for knowledge classification
Diabetes mellitus is now recognised as a major worldwide
public health problem. At present, about 100
million people are registered as diabetic patients. Many
clinical, social and economic problems occur as a
consequence of insulin-dependent diabetes. Treatment
attempts to prevent or delay complications by applying
‘optimal’ glycaemic control. Therefore, there is a
continuous need for effective monitoring of the patient.
Given the popularity of decision tree learning
algorithms as well as neural networks for knowledge
classification which is further used for decision
support, this paper examines their relative merits by
applying one algorithm from each family on a medical
problem; that of recommending a particular diabetes
regime. For the purposes of this study, OC1 a
descendant of Quinlan’s ID3 algorithm was chosen as
decision tree learning algorithm and a generating
shrinking algorithm for learning arbitrary
classifications as a neural network algorithm. These
systems were trained on 646 cases derived from two
countries in Europe and were tested on 100 cases
which were different from the original 646 cases
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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
Integration of decision support systems to improve decision support performance
Decision support system (DSS) is a well-established research and development area. Traditional isolated, stand-alone DSS has been recently facing new challenges. In order to improve the performance of DSS to meet the challenges, research has been actively carried out to develop integrated decision support systems (IDSS). This paper reviews the current research efforts with regard to the development of IDSS. The focus of the paper is on the integration aspect for IDSS through multiple perspectives, and the technologies that support this integration. More than 100 papers and software systems are discussed. Current research efforts and the development status of IDSS are explained, compared and classified. In addition, future trends and challenges in integration are outlined. The paper concludes that by addressing integration, better support will be provided to decision makers, with the expectation of both better decisions and improved decision making processes
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