A Novel Multi-Stage Feature Selection and Classification Model for Accurate Liver Disease Detection

Liver disease, comprising a spectrum of conditions that afflict the liver, stands as a significant health challenge with global implications. This paper presents an effective and highly accurate liver disease detection model wherein unique feature Selection and classification techniques are implemented. The novelty contribution of this work is effective feature selection technique in which features are selected in three stages. In the first stage, entropy, Eigenvector centrality and entropy-correlation based techniques are implemented on each feature to determine their relevance score. These features are then combined and their average value is calculated to form the first feature score vector. In the second stage, Randon Forest (RF) classifier is used for analysing the effectiveness of features selected in first stage in terms of their accuracy. Based on this accuracy, the relevance score of features is again updated to form the second feature set. In the third stage, Fuzzy model is used for determining the contextual relevance among various features. The selected feature set is then passed to proposed BELV classification model, wherein techniques like bagging, ensemble learning and voting is applied. Three baseline classifiers i.e., KNN, RF and Decision Tree (DT) are used in ensemble learning to make individual predictions which are then combined before applying majority voting mechanism to make the final prediction. The efficacy of proposed model is tested on ILPD and CPD datasets for binary classification and multi-stage disease detection respectively. Through extensive experiments in MATLAB software proposed model attained an accuracy of 93% and 96.8% for binary and multi-stage disease classifications respectively.