A Novel Prediction Approach for Effective Medical Data Mining

Data mining techniques have been employed for solving many medical problems, especially for disease prediction. For instance, given a dataset containing normal and cancerous patients, the goal is to develop a model to predict whether a new (unknown) patient belongs to the normal or cancerous class. In general, the model is constructed based on some machine learning technique over a collected training set. However, the quality of the training set can affect the final prediction performance of the model. That is, if the training set contains some certain amount of noisy data (or outliers), then the model's performance could be degraded. In literature, instance selection is performed over a given training set in order to filter out some noisy data and the reduced training set containing non-noisy data is used for developing the prediction model. In this paper, we present a novel approach where instance selection is performed to divide a given training set into noisy and non-noisy subsets. Then, they are used to train two models respectively. During prediction, the instance selection step is also executed over the testing set, in which the noisy and non-noisy subsets are used to test their corresponding models respectively. The experimental results based on various medical domain datasets show that our proposed approach performs better than the baseline, which is based on the conventional instance selection approach.