Performance Evaluations of Diagnostic Prediction with Neural Networks with Data Filters in Different Types

The advent of the information age and the rapid development of IT skills have led to the construction of massive databases, thus the current research focus is shifting to the efficient utilization of these vast volumes of stored information. Among the data mining algorithms that have been applied to this problem, neural networks can be used with various types, qualities, distributions, or volumes of data and they have high predictive power. Thus, neural networks are known to be the most useful and extensible algorithms, whereas logistic analysis has many constraints. In addition, neural networks obtain better results when the assumptions of linear discriminant analysis cannot be satisfied. The present study evaluated a multilayer perceptron (MLP) and radial-basis function network (RBFN), and their performance levels were compared with logistic regression based on cross-validation using the same data. The experiments showed that MLP delivered better performance than other methods in medical diagnostic applications where numerical data are used. MLP also performed better with the heart disease dataset using finely specified data types compared with the diabetes dataset using simple data types.

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