In machine learning and data science, feature selection is considered as a crucial step of data preprocessing. When we directly apply the raw data for classification or clustering purposes, sometimes we observe that the learning algorithms do not perform well. One possible reason for this is the presence of redundant, noisy, and non-informative features or attributes in the datasets. Hence, feature selection methods are used to identify the subset of relevant features that can maximize the model performance. Moreover, due to reduction in feature dimension, both training time and storage required by the model can be reduced as well. In this paper, we present a tri-stage wrapper-filter-based feature selection framework for the purpose of medical report-based disease detection. In the first stage, an ensemble was formed by four filter methods—Mutual Information, ReliefF, Chi Square, and Xvariance—and then each feature from the union set was assessed by three classification algorithms—support vector machine, naïve Bayes, and k-nearest neighbors—and an average accuracy was calculated. The features with higher accuracy were selected to obtain a preliminary subset of optimal features. In the second stage, Pearson correlation was used to discard highly correlated features. In these two stages, XGBoost classification algorithm was applied to obtain the most contributing features that, in turn, provide the best optimal subset. Then, in the final stage, we fed the obtained feature subset to a meta-heuristic algorithm, called whale optimization algorithm, in order to further reduce the feature set and to achieve higher accuracy. We evaluated the proposed feature selection framework on four publicly available disease datasets taken from the UCI machine learning repository, namely, arrhythmia, leukemia, DLBCL, and prostate cancer. Our obtained results confirm that the proposed method can perform better than many state-of-the-art methods and can detect important features as well. Less features ensure less medical tests for correct diagnosis, thus saving both time and cost.

A facility for early disease diagnosis is very important in the medical field. Since most disease datasets are huge in dimension, an automatic diagnosis process through computing devices becomes complex and time-consuming. Feature selection methods can be used to eliminate unnecessary information from a dataset. Among the existing filter-based feature selection methods, one filter may end up eliminating important features. This is where our ensemble-based filter methods of Mutual Information, ReliefF, and Chi-Square come into play, where even if one filter eliminates an important feature, another filter may compensate for it. This has been done by forming a union of the reduced set produced by each filter. The three datasets on which the evaluation has been done are PID, DLBCL and Prostate cancer. From the union, accuracies are calculated using different classifiers and the classification accuracies of 97.18%, 97.32% and 91.90% are achieved in the three datasets, respectively.