Name
Abstract | ||
|---|---|---|
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. |
Year | DOI | Venue |
|---|---|---|
2011 | 10.1007/s10044-011-0224-z | Pattern Anal. Appl. |
Keywords | Field | DocType |
feature space | Relational space,Feature vector,Pattern recognition,Malaria,Artificial intelligence,Machine learning,Mathematics | Journal |
Volume | Issue | ISSN |
14 | 3 | 1433-755X |
Citations | PageRank | References |
1 | 0.48 | 5 |
Authors | ||
2 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Paolo Pintus | 1 | 1 | 0.82 |
| Maria Petrou | 2 | 2270 | 223.83 |