Study of machine learning techniques for outcome assessment of leptospirosis patients
Abstract Leptospirosis is a global disease that impacts people worldwide, particularly in humid and tropical regions, and is associated with significant socio-economic deficiencies. Its symptoms are often confused with other syndromes, which can compromise clinical diagnosis and the failure to carry...
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Nature Portfolio
2024-06-01
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| Series: | Scientific Reports |
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| Online Access: | https://doi.org/10.1038/s41598-024-62254-1 |
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| author | Andreia Ferreira da Silva Karla Figueiredo Igor W. S. Falcão Fernando A. R. Costa Marcos César da Rocha Seruffo Carla Cristina Guimarães de Moraes |
| author_facet | Andreia Ferreira da Silva Karla Figueiredo Igor W. S. Falcão Fernando A. R. Costa Marcos César da Rocha Seruffo Carla Cristina Guimarães de Moraes |
| author_sort | Andreia Ferreira da Silva |
| collection | DOAJ |
| description | Abstract Leptospirosis is a global disease that impacts people worldwide, particularly in humid and tropical regions, and is associated with significant socio-economic deficiencies. Its symptoms are often confused with other syndromes, which can compromise clinical diagnosis and the failure to carry out specific laboratory tests. In this respect, this paper presents a study of three algorithms (Decision Tree, Random Forest and Adaboost) for predicting the outcome (cure or death) of individuals with leptospirosis. Using the records contained in the government National System of Aggressions and Notification (SINAN, in portuguese) from 2007 to 2017, for the state of Pará, Brazil, where the temporal attributes of health care, symptoms (headache, vomiting, jaundice, calf pain) and clinical evolution (renal failure and respiratory changes) were used. In the performance evaluation of the selected models, it was observed that the Random Forest exhibited an accuracy of 90.81% for the training dataset, considering the attributes of experiment 8, and the Decision Tree presented an accuracy of 74.29 for the validation database. So, this result considers the best attributes pointed out by experiment 10: time first symptoms medical attention, time first symptoms ELISA sample collection, medical attention hospital admission time, headache, calf pain, vomiting, jaundice, renal insufficiency, and respiratory alterations. The contribution of this article is the confirmation that artificial intelligence, using the Decision Tree model algorithm, depicting the best choice as the final model to be used in future data for the prediction of human leptospirosis cases, helping in the diagnosis and course of the disease, aiming to avoid the evolution to death. |
| format | Article |
| id | doaj-art-45cc67b0e9044c37a1e919fb6c74a7a9 |
| institution | Kabale University |
| issn | 2045-2322 |
| language | English |
| publishDate | 2024-06-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj-art-45cc67b0e9044c37a1e919fb6c74a7a92025-01-05T12:27:52ZengNature PortfolioScientific Reports2045-23222024-06-0114111110.1038/s41598-024-62254-1Study of machine learning techniques for outcome assessment of leptospirosis patientsAndreia Ferreira da Silva0Karla Figueiredo1Igor W. S. Falcão2Fernando A. R. Costa3Marcos César da Rocha Seruffo4Carla Cristina Guimarães de Moraes5Laboratory of Zoonoses and Public Health - Federal University of ParaDepartment of Informatics and Computer Science, Institute of Mathematics and Statistics, Rio de Janeiro State UniversityFederal University of ParaFederal University of ParaFederal University of ParaLaboratory of Zoonoses and Public Health - Federal University of ParaAbstract Leptospirosis is a global disease that impacts people worldwide, particularly in humid and tropical regions, and is associated with significant socio-economic deficiencies. Its symptoms are often confused with other syndromes, which can compromise clinical diagnosis and the failure to carry out specific laboratory tests. In this respect, this paper presents a study of three algorithms (Decision Tree, Random Forest and Adaboost) for predicting the outcome (cure or death) of individuals with leptospirosis. Using the records contained in the government National System of Aggressions and Notification (SINAN, in portuguese) from 2007 to 2017, for the state of Pará, Brazil, where the temporal attributes of health care, symptoms (headache, vomiting, jaundice, calf pain) and clinical evolution (renal failure and respiratory changes) were used. In the performance evaluation of the selected models, it was observed that the Random Forest exhibited an accuracy of 90.81% for the training dataset, considering the attributes of experiment 8, and the Decision Tree presented an accuracy of 74.29 for the validation database. So, this result considers the best attributes pointed out by experiment 10: time first symptoms medical attention, time first symptoms ELISA sample collection, medical attention hospital admission time, headache, calf pain, vomiting, jaundice, renal insufficiency, and respiratory alterations. The contribution of this article is the confirmation that artificial intelligence, using the Decision Tree model algorithm, depicting the best choice as the final model to be used in future data for the prediction of human leptospirosis cases, helping in the diagnosis and course of the disease, aiming to avoid the evolution to death.https://doi.org/10.1038/s41598-024-62254-1LeptospirosisData miningMachine learningDesfechoSimulatorDecision tree |
| spellingShingle | Andreia Ferreira da Silva Karla Figueiredo Igor W. S. Falcão Fernando A. R. Costa Marcos César da Rocha Seruffo Carla Cristina Guimarães de Moraes Study of machine learning techniques for outcome assessment of leptospirosis patients Scientific Reports Leptospirosis Data mining Machine learning Desfecho Simulator Decision tree |
| title | Study of machine learning techniques for outcome assessment of leptospirosis patients |
| title_full | Study of machine learning techniques for outcome assessment of leptospirosis patients |
| title_fullStr | Study of machine learning techniques for outcome assessment of leptospirosis patients |
| title_full_unstemmed | Study of machine learning techniques for outcome assessment of leptospirosis patients |
| title_short | Study of machine learning techniques for outcome assessment of leptospirosis patients |
| title_sort | study of machine learning techniques for outcome assessment of leptospirosis patients |
| topic | Leptospirosis Data mining Machine learning Desfecho Simulator Decision tree |
| url | https://doi.org/10.1038/s41598-024-62254-1 |
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