Hybrid cluster based classification model for high dimensional disease prediction databases

Abstract

As biomedical databases continue to expand, it becomes increasingly difficult to identify a crucial feature for a classification task due to big data size and sparsity issues. Traditional feature subset models rely on fixed-sized dimensions for the feature ranking and classification process, which is not suitable for addressing concerns with sparsity, missing values, and imbalance in the selection of crucial features for the data classification process. To enhance disease prediction effectiveness, this article proposes a hybrid ensemble feature selection method that employs an advanced cluster-based classification model. The model uses an ensemble of rated features to classify the disease with high accuracy and true positive rate. To improve the effectiveness of tree pruning and classification, we introduce a novel cluster-based classification model. We simulated experimental results using various training datasets to predict accuracy. Our proposed results demonstrate that the gene-chemical disease clustering-based classification framework outperforms traditional methods, statistical metrics, and classification models in terms of optimization.