Visual instance-based recommendation system for medical data mining
Joris Falip, Amine Aït Younes, Frédéric Blanchard, Brigitte Delemer, Alpha Diallo, Michel Herbin · Procedia Computer Science · 2017
This paper presents an instance-based algorithm allowing exploration of large medical dataset by making pairwise connection between patients. In our metric-free method, each individual in a dataset ranks every member of the dataset. By aggregating these ranks, it is then possible to visualize data according to typical individuals representing subsets of closely-related patients. The paper also describes a visualization tool allowing exploration of a database of diabetic patients. This prototype of a recommendation system implements the aforementioned algorithm to enrich data, structure patients, create associations between individuals and provide recommendations.