Differential diagnosis of hemorrhagic fevers using ARTMAP and an Artificial Immune System
The differential diagnosis of endemic hemorrhagic fevers in tropical countries is by no means an easy task for medical practitioners. Several diseases often overlap with others in terms of signs and symptoms, thus making this diagnosis a difficult, error-prone process. Machine Learning algorithms po...
- Autores:
- Tipo de recurso:
- Fecha de publicación:
- 2013
- Institución:
- Universidad Tecnológica de Bolívar
- Repositorio:
- Repositorio Institucional UTB
- Idioma:
- eng
- OAI Identifier:
- oai:repositorio.utb.edu.co:20.500.12585/9075
- Acceso en línea:
- https://hdl.handle.net/20.500.12585/9075
- Palabra clave:
- ARTMAP
Dengue
Differential diagnosis
Hemorrhagic fever
Leptospirosis
Machine learning
Malaria
Neural networks
- Rights
- restrictedAccess
- License
- http://creativecommons.org/licenses/by-nc-nd/4.0/
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Differential diagnosis of hemorrhagic fevers using ARTMAP and an Artificial Immune System |
| title |
Differential diagnosis of hemorrhagic fevers using ARTMAP and an Artificial Immune System |
| spellingShingle |
Differential diagnosis of hemorrhagic fevers using ARTMAP and an Artificial Immune System ARTMAP Dengue Differential diagnosis Hemorrhagic fever Leptospirosis Machine learning Malaria Neural networks |
| title_short |
Differential diagnosis of hemorrhagic fevers using ARTMAP and an Artificial Immune System |
| title_full |
Differential diagnosis of hemorrhagic fevers using ARTMAP and an Artificial Immune System |
| title_fullStr |
Differential diagnosis of hemorrhagic fevers using ARTMAP and an Artificial Immune System |
| title_full_unstemmed |
Differential diagnosis of hemorrhagic fevers using ARTMAP and an Artificial Immune System |
| title_sort |
Differential diagnosis of hemorrhagic fevers using ARTMAP and an Artificial Immune System |
| dc.subject.keywords.none.fl_str_mv |
ARTMAP Dengue Differential diagnosis Hemorrhagic fever Leptospirosis Machine learning Malaria Neural networks |
| topic |
ARTMAP Dengue Differential diagnosis Hemorrhagic fever Leptospirosis Machine learning Malaria Neural networks |
| description |
The differential diagnosis of endemic hemorrhagic fevers in tropical countries is by no means an easy task for medical practitioners. Several diseases often overlap with others in terms of signs and symptoms, thus making this diagnosis a difficult, error-prone process. Machine Learning algorithms possess some useful qualities to tackle this kind of pattern recognition problems. In this paper, a neural-network-based approach to the differential diagnosis of Dengue Fever, Leptospirosis and Malaria, using the Adaptive Resonance Theory Map (ARTMAP) family is discussed. The use of an Artificial Immune System (CLONALG) led to the identification of a subset of symptoms that enhanced the performance of the classifiers considered. Training, validation and testing phases were conducted using a dataset consisting of medical charts from patients treated in the last 10 years at Napoleón Franco Pareja Children Hospital in Cartagena, Colombia. Results obtained on the test set are promising, and support the feasibility of this approach. © 2013 by IJAI. |
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2013 |
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2013 |
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2020-03-26T16:32:53Z |
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2020-03-26T16:32:53Z |
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http://purl.org/coar/version/c_970fb48d4fbd8a85 |
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info:eu-repo/semantics/article |
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Artículo |
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International Journal of Artificial Intelligence; Vol. 11, Núm. 13 A; pp. 150-169 |
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09740635 |
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https://hdl.handle.net/20.500.12585/9075 |
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Universidad Tecnológica de Bolívar |
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Repositorio UTB |
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55782426500 55783129400 55782490400 |
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International Journal of Artificial Intelligence; Vol. 11, Núm. 13 A; pp. 150-169 09740635 Universidad Tecnológica de Bolívar Repositorio UTB 55782426500 55783129400 55782490400 |
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https://hdl.handle.net/20.500.12585/9075 |
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eng |
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2020-03-26T16:32:53Z2020-03-26T16:32:53Z2013International Journal of Artificial Intelligence; Vol. 11, Núm. 13 A; pp. 150-16909740635https://hdl.handle.net/20.500.12585/9075Universidad Tecnológica de BolívarRepositorio UTB557824265005578312940055782490400The differential diagnosis of endemic hemorrhagic fevers in tropical countries is by no means an easy task for medical practitioners. Several diseases often overlap with others in terms of signs and symptoms, thus making this diagnosis a difficult, error-prone process. Machine Learning algorithms possess some useful qualities to tackle this kind of pattern recognition problems. In this paper, a neural-network-based approach to the differential diagnosis of Dengue Fever, Leptospirosis and Malaria, using the Adaptive Resonance Theory Map (ARTMAP) family is discussed. The use of an Artificial Immune System (CLONALG) led to the identification of a subset of symptoms that enhanced the performance of the classifiers considered. Training, validation and testing phases were conducted using a dataset consisting of medical charts from patients treated in the last 10 years at Napoleón Franco Pareja Children Hospital in Cartagena, Colombia. Results obtained on the test set are promising, and support the feasibility of this approach. © 2013 by IJAI.Recurso electrónicoapplication/pdfenghttp://creativecommons.org/licenses/by-nc-nd/4.0/info:eu-repo/semantics/restrictedAccessAtribución-NoComercial 4.0 Internacionalhttp://purl.org/coar/access_right/c_16echttps://www.scopus.com/inward/record.uri?eid=2-s2.0-84879759875&partnerID=40&md5=e126f6afb294df43dee6cf023435ca5aDifferential diagnosis of hemorrhagic fevers using ARTMAP and an Artificial Immune Systeminfo:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionArtículohttp://purl.org/coar/version/c_970fb48d4fbd8a85http://purl.org/coar/resource_type/c_2df8fbb1ARTMAPDengueDifferential diagnosisHemorrhagic feverLeptospirosisMachine learningMalariaNeural networksCaicedo Torres W.Quintana Álvarez, Moisés RamónPinzón H.Brown, M., Vickers, I., Salas, R., Smikle, M., Leptospirosis in suspected cases of dengue in Jamaica (2010) 2002-2007., Tropical doctor, 40 (2), pp. 92-94Burnet, F., (1959) The clonal selection theory of acquired immunity, , Abraham Flexner lectures, Vanderbilt University PressCarpenter, G., Default artmap (2003) Proceedings of the International Joint Conference on Neural Networks 2003, 2, pp. 1396-1401Carpenter, G., Grossberg, S., Art 2: Self-organization of stable category recognition codes for analog input patterns (1987) Appl Opt, 26 (23), pp. 4919-4930Carpenter, G., Grossberg, S., A massively parallel architecture for a self-organizing neural pattern recognition machine (1987) Computer Vision, Graphics, and Image Processing, 37 (1), pp. 54-115Carpenter, G., Grossberg, S., Art3: Hierarchical search using chemical transmitters in self-organizing pattern recognition architectures (1990) Neural Networks, 3 (2), pp. 129-152Carpenter, G., Grossberg, S., Markuzon, N., Reynolds, J., Rosen, D., Fuzzy artmap: A neural network architecture for incremental supervised learning of analog multidimensional maps (1992) IEEE Trans Neural Networks, 3 (5), pp. 698-713Carpenter, G., Grossberg, S., Rosen, D., Fuzzy art: Fast stable learning and categorization of analog patterns by an adaptive resonance system (1991) Neural Netw, 4, pp. 759-771Carpenter, G., Markuzon, N., Artmap-ic and medical diagnosis: Instance counting and inconsistent cases (1998) Neural Networks, pp. 323-336Carpenter, G., Milenova, B., Noeske, B., Distributed artmap: A neural network for fast distributed supervised learning (1998) Neural Networks, 11 (5), pp. 793-813Chadwick, D., Arch, B., Wilder-Smith, A., Paton, N., Distinguishing dengue fever from other infections on the basis of simple clinical and laboratory features: Application of logistic regression analysis (2006) Journal of Clinical Virology, 35 (2), pp. 147-153De Castro, L.N., Von Zuben, F.J., Learning and optimization using the clonal selection principle (2002) IEEE Transactions on Evolutionary Computation, 6 (3), pp. 239-251Downs, J., Harrison, R., Kennedy, R., Cross, S., Application of the fuzzy artmap neural network model to medical pattern classification tasks (1996) Artificial Intelligence in Medicine, 8 (4), pp. 403-428Ellis, T., Imrie, A., Katz, A., Effler, P., Underrecognition of leptospirosis during a dengue fever outbreak in Hawaii (2008) 2001-2002., Vector borne and zoonotic diseases (Larchmont, N.Y.), 8 (4), pp. 541-547Goodman, P., Kaburlasos, V., Egbert, D., Carpenter, G., Grossberg, S., Reynolds, J., Rosen, D., Hartz, A., Fuzzy artmap neural network compared to linear discriminant analysis prediction of the length of hospital stay in patients with pneumonia (1992) Proceedings of the IEEE 1992 Intl Conf. on Systems, Man and Cybernetics, 1, pp. 748-753Halstead, S.E., (2008) Dengue, , Tropical Medicine: Science and Practice, Imperial College PressHornik, K., Stinchcombe, M., White, H., Multilayer feedforward networks are universal approximators (1989) Neural Netw, 2, pp. 359-366Kasuba, T., Simplified fuzzy artmap (1993) AI Expert, 8, pp. 19-25Kohonen, T., Self-organized formation of topologically correct feature maps (1982) Biological Cybernetics, 43, pp. 59-69. , 10.1007/BF00337288Levett, P., Branch, S., Edwards, C., Detection of dengue infection in patients investigated for leptospirosis in barbados (2000) Am J Trop Med Hyg, 62 (1), pp. 112-114Libraty, D.H., Myint, K.S.A., Murray, C.K., Gibbons, R.V., Mammen, M.P., Endy, T.P., Li, W., Ennis, F.A., A comparative study of leptospirosis and dengue in thai children (2007) PLoS Negl Trop Dis, 1 (3), pp. e111Mandell, D.E., Bennet, J.E., Kgrostad, D.A., Principles and Practice of Infectious Diseases (2000) Churchill Livingstone, pp. 2818-2831Mandell, D.E., Bennet, J.E., Tappero, J.A., Ashford, D.A., Perkins, B.A., (2000) Principles and Practice of Infectious Diseases, pp. 2495-2501. , Churchill LivingstoneMandell, D.E., Bennet, J.E., Tsai, T.A., (2000) Principles and Practice of Infectious Diseases, pp. 1715-1736. , Churchill LivingstoneMarkuzon, N., Gaehde, S., Ash, A., Carpenter, G., Moskowitz, M., Predicting risk of an adverse event in complex medical data sets using fuzzy artmap network (1994) Artificial Intelligence in Medicine: Interpreting Clinical Data, pp. 93-96. , Technical Report SeriesMoore, B., Art 1 and pattern clustering (1989) Proceedings of the 1988 Connectionist Models Summer School by David Touretzky, pp. 174-185. , Geoffrey Hinton and Terrence SejnowskiPotts, J., Rothman, A., Clinical and laboratory features that distinguish dengue from other febrile illnesses in endemic populations (2008) Tropical medicine international health TM IH, 13 (11), pp. 1328-1340Rico-Hesse, R., Molecular evolution and distribution of dengue viruses type 1 and 2 in nature (1990) Virology, 174 (2), pp. 479-493Roche, J.E.A., Isolement de 96 souches de virus dengue 2 partir de mosquiques captures en cote-d'ivoire et haute-volta (1983) Ann Virol, 134, pp. 233-244Rudnick, A., Lim, T., (1986) Dengue fever studies in malaysia, pp. 127-197. , Inst Med Res Malaysia Bull (23)Traore-Lamizana, M., Zeller, H., Monlun, E., Mondo, M., Hervy, J., Adam, F., Digoutte, J., Dengue 2 outbreak in southeastern senegal during 1990: Virus isolations from mosquitoes (diptera: Culicidae) (1994) Journal of Medical Entomology, 31 (4), pp. 623-627Yang, Y., An evaluation of statistical approaches to text categorization (1999) Information Retrieval, 1, pp. 69-90. , 10.1023/A:1009982220290Zadeh, L., Fuzzy sets (1965) Information Control, 8, pp. 338-353http://purl.org/coar/resource_type/c_6501THUMBNAILMiniProdInv.pngMiniProdInv.pngimage/png23941https://repositorio.utb.edu.co/bitstream/20.500.12585/9075/1/MiniProdInv.png0cb0f101a8d16897fb46fc914d3d7043MD5120.500.12585/9075oai:repositorio.utb.edu.co:20.500.12585/90752023-04-24 09:35:15.807Repositorio Institucional UTBrepositorioutb@utb.edu.co |