Visual instance-based recommendation system for medical data mining

Abstract

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.

Joris Falip

Associate professor

My research interests include artificial intelligence, exploratory data analysis and coding.