Relational space classification for malaria diagnosis
We present a study of sera derived from the malaria medical analysis of 189 subjects. The feature space is 18-dimensional and each serum is represented by a binary number. The subjects are divided into three different groups: no malaria, clinical malaria and asymptomatic subjects. We studied the main characteristics of the data and we selected 7 out of the 18 antigens as the most important for group discrimination. We propose a novel representation of the data in the so-called relational space, where the coded data of pairs of patients are plotted. We are able to separate the groups with 58% accuracy, about 15% points better than several conventional methods with which we compare our results.
relational space classification malaria gene expression