Ordinal classification of depression spatial hot-spots of prevalence

Research areas:

• Methodology - Machine learning

Year:

2011

Type of Publication:

In Proceedings

Keywords:

ordinal regression, spatial distribution of illnesses, geographical information systems, kernel discriminant learning

Authors:

• Pérez-Ortiz, María
• Gutiérrez, Pedro Antonio
• García-Alonso, Carlos
• Carulla, L. Salvador
• Pérez, J. A. Salinas
• Hervás-Martínez, César

Book title:

11th International Conference on Intelligent Systems Design andApplications (ISDA 2011)

Pages:

1170-1175

BibTex:

@conference{162011, author = "Mar{\'i}a P{\'e}rez-Ortiz and Pedro Antonio Guti{\'e}rrez and Carlos Garc{\'i}a-Alonso and L. Salvador Carulla and J.A. Salinas P{\'e}rez and C{\'e}sar Herv{\'a}s-Mart{\'i}nez", abstract = "In this paper we apply and test a recent ordinal algorithm for classification (Kernel Discriminant Learning Ordinal Regression, KDLOR), in order to recognize a group of geographically close spatial units with a similar prevalence pattern significantly high (or low), which are called hot- spots (or cold-spots). Different spatial analysis techniques have been used for studying geographical distribution of a specific illness in mental health-care because it could be useful to organize the spatial distribution of health-care services. Ordinal classification is used in this problem because the classes are: spatial unit with depression, spatial unit which could present depression and spatial unit where there is not depression. It is shown that the proposed method is capable of preserving the rank of data classes in a projected data space for this database. In comparison to other standard methods like C4.5, SVMRank, Adaboost, and MLP nominal classifiers, the proposed KDLOR algorithm is shown to be competitive. ", booktitle = "11th International Conference on Intelligent Systems Design andApplications (ISDA 2011)", keywords = "ordinal regression, spatial distribution of illnesses, geographical information systems, kernel discriminant learning", pages = "1170-1175", title = "{O}rdinal classification of depression spatial hot-spots of prevalence", year = "2011", }

Abstract:

In this paper we apply and test a recent ordinal algorithm for classification (Kernel Discriminant Learning Ordinal Regression, KDLOR), in order to recognize a group of geographically close spatial units with a similar prevalence pattern significantly high (or low), which are called hot- spots (or cold-spots). Different spatial analysis techniques have been used for studying geographical distribution of a specific illness in mental health-care because it could be useful to organize the spatial distribution of health-care services. Ordinal classification is used in this problem because the classes are: spatial unit with depression, spatial unit which could present depression and spatial unit where there is not depression. It is shown that the proposed method is capable of preserving the rank of data classes in a projected data space for this database. In comparison to other standard methods like C4.5, SVMRank, Adaboost, and MLP nominal classifiers, the proposed KDLOR algorithm is shown to be competitive.