Abstract
Medical data classification is a prime data mining problem being discussed about for a decade that has attracted several researchers around the world. Most classifiers are designed so as to learn from the data itself using a training process, because complete expert knowledge to determine classifier parameters is impracticable. This paper proposes a hybrid methodology based on machine learning paradigm. This paradigm integrates the successful exploration mechanism called self-regulated learning capability of the particle swarm optimization (PSO) algorithm with the extreme learning machine (ELM) classifier. As a recent off-line learning method, ELM is a single-hidden layer feedforward neural network (FFNN), proved to be an excellent classifier with large number of hidden layer neurons. In this research, PSO is used to determine the optimum set of parameters for the ELM, thus reducing the number of hidden layer neurons, and it further improves the network generalization performance. The proposed method is experimented on five benchmarked datasets of the UCI Machine Learning Repository for handling medical dataset classification. Simulation results show that the proposed approach is able to achieve good generalization performance, compared to the results of other classifiers.
Highlights
In recent times, the application of computational or machine intelligence in medical diagnosis is a new trend for large medical data applications
The performance of the proposed SRLPSO-extreme learning machine (ELM) method is experimented on five real benchmark classification problems (UCI Machine Learning Repository)
Since the considered application is of medical dataset involving complex data, the classification should be carried out in an accurate manner
Summary
The application of computational or machine intelligence in medical diagnosis is a new trend for large medical data applications. Most of the diagnosis techniques in medical field are systematized as intelligent data classification approaches. Among the various assignments performed by a CAD system, classification is most common, where a tag is allocated to a query case (i.e., a patient) based on chosen number of features (i.e., medical findings). Medical database classification problem may be categorized as a class of complex optimization problem with an objective to guarantee the diagnosis aid accurately. Aside from other traditional classification problems, medical dataset classification problems are applied in future diagnosis. Patients or doctors are not completely informed about the cause (classification result) of the disease, and will be made known of the symptoms that derive the cause of disease, which is the most important of the medical dataset classification problem
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