  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, as a part of a
disease management system, that identifies and recommends the most important
risk factors for an illness.
  Methods: A novel mutual information and ensemble-based feature ranking
approach for identifying critical risk factors in healthcare prognosis is
proposed.
  Results: To establish the effectiveness of the proposed method, experiments
have 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. The method is able to recommend all relevant critical risk
factors for ccRCC. It also attains a higher accuracy (96.6% and 98.6% using
support vector machine and neural network, respectively) for ccRCC staging 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 can identify and recommend risk factors
that have the most discriminating power for detecting diseases.
