An intelligent temporal pattern classification system using fuzzy temporal rules and particle swarm optimization (original) (raw)
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Clinical decision making by health professionals is a more complex process The Clinical decision support system (CDSS) is an interactive decision support system (DSS) Computer Software, which is designed to assist physicians and other health professionals with decision making tasks, such as determining diagnosis of patient data. For this work lot of researcher have proposed many algorithms based on cost, efficiency, time consumption but not in good accuracy. The proposed system based on K-means clustering and Artificial Neural Network using Particle Swarm Optimization Algorithm will maximize the accuracy and efficiency of clinical decision-making with minimizing time consumption & cost. Proposed system having two main operations one is k-means clustering for grouping the patient data according to the symptom and patient details,second one is the adaptation of network weights using Particle Swarm Optimization (PSO) was proposed as a mechanism to improve the performance of Artificial ...
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Classification analysis is widely adopted for healthcare applications to support medical diagnostic decisions, improving quality of patient care, etc. A subset dataset of the extensive amounts of data stored in medical databases is selected for training. If the training dataset contains irrelevant features, classification analysis may produce less accurate and less understandable results. Feature subset selection is one of data preprocessing step, which is of immense importance in the field of data mining. This paper proposes the filter and wrapper approaches with Particle Swarm Optimization (PSO) as a feature selection methods for medical data. The performance of the proposed methods is compared with another feature selection algorithm based on Genetic approach. The two algorithms are applied to three medical data sets The results show that the feature subset recognized by the proposed PSO when given as input to five classifiers, namely decision tree, Naïve Bayes, Bayesian, Radial basis function and k-nearest neighbor classifiers showed enhanced classification accuracy over all given types of classification methods.
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