A Nomogram Construction Method Using Genetic Algorithm and Naïve Bayesian Technique

In medical practice, the diagnosis or prediction models requiring complicated computations are not widely recognized due to difficulty in interpreting the course of reasoning and the complexity of computations. Medical personnel have used the nomograms which are a graphical representation for numerical relationships that enables to easily compute a complicated function without help of computation machines. It has been widely paid attention in diagnosing diseases or predicting the progress of diseases. A nomogram is constructed from a set of clinical data which contain various attributes such as symptoms, lab experiment results, therapy history, progress of diseases or identification of diseases. It is of importance to select effective ones from available attributes, sometimes along with parameters accompanying the attributes. This paper introduces a nomogram construction method that uses a naïve Bayesian technique to construct a nomogram as well as a genetic algorithm to select effective attributes and parameters. Key-Words: nomogram, genetic algorithm, naïve Bayesian learning, medical data analysis, machine learning