MUTE-Reco: MUTual Information Assisted Ensemble Feature RECOmmender System for Healthcare Prognosis

Purpose: Health recommenders act as important decision support systems, aiding patients and medical professionals in taking actions that lead to patients' well-being. These systems extract the information which may be of particular relevance to the end-user, helping them in making appropriate decisions. The present study proposes a feature recommender that identifies and recommends the most important risk factors for healthcare prognosis. Methods: A novel mutual information and ensemble-based feature ranking approach (termed as, MUTE-Reco) considering the rank of features obtained from eight popular feature selection methods, is proposed. Results: To establish the effectiveness of the proposed method, the experiment has been conducted on four benchmark datasets of diverse diseases (clear cell renal cell carcinoma (ccRCC), chronic kidney disease, Indian liver patient, and cervical cancer risk factors). The performance of the proposed recommender is compared with four state-of-the-art methods using recommender systems' performance metrics like average precision@K, precision@K, recall@K, F1@K, reciprocal rank@K. Experimental results show that the model built with the recommended features can attain a higher accuracy (96.6% and 98.6% using support vector machine and neural network, respectively) for classifying different stages of ccRCC with a reduced feature set as compared to existing methods. Moreover, the top two features recommended using the proposed method with ccRCC, viz. size of tumor and metastasis status, are medically validated from the existing TNM system. Results are also found to be superior for the other three datasets. Conclusion: The proposed recommender, MUTE-Reco, can identify and recommend risk factors that have the most discriminating power for detecting diseases.