Visual analytics for classifier construction and evaluation for medical data

Abstract

Designing and optimizing classifiers for multidimensional mixed quantitative-and-categorical data is a challenging task. We present here a workflow and associated toolset that assists with this task, by providing the designer with insights into how the multidimensional input data is structured and how this structure influences the classification results. Our approach heavily relies on visual analytics for detecting relevant patterns in the input data, observing the distribution of classification errors, detecting and controlling the effect of feature selection on the classification results, and comparing in detail the performance of different classification techniques. We demonstrate the value of our approach on the concrete problem of building a classifier for predicting biochemical recurrence, indicating potential cancer relapse after prostate cancer treatment, from clinical patient data.