Machine learning algorithms to predict the 1 year unfavourable prognosis for advanced schistosomiasis |
| |
| Institution: | 1. Fudan University School of Public Health, Building 8, 130 Dong’an Road, Shanghai 200032, China;2. Key Laboratory of Public Health Safety, Fudan University, Ministry of Education, Building 8, 130 Dong’an Road, Shanghai 200032, China;3. Fudan University Center for Tropical Disease Research, Building 8, 130 Dong’an Road, Shanghai 200032, China;4. Hunan Institute for Schistosomiasis Control, Yueyang, Hunan Province, China;5. School of Epidemiology and Public Health, Faculty of Medicine, University of Ottawa, 600 Peter Morand Crescent, Ottawa, Ontario K1G 5Z3, Canada;1. Department of Epidemiology and Statistics, School of Public Health, Soochow University, Suzhou, China;2. Key Laboratory of National Health and Family Planning Commission on Parasitic Disease Control and Prevention, Jiangsu Provincial Key Laboratory on Parasite and Vector Control Technology, Jiangsu Institute of Parasitic Diseases, Wuxi, China;3. Centre for Emerging, Endemic and Exotic Diseases (CEEED), Department of Pathology and Population Sciences, Royal Veterinary College, University of London, London, United Kingdom;1. Mitrani Department of Desert Ecology, Swiss Institute of Dryland Environmental and Energy Research, Jacob Blaustein Institutes for Desert Research, Ben-Gurion University of the Negev, Sede Boqer Campus, Midreshet Ben-Gurion, Israel;2. Agricultural Research Council-Onderstepoort Veterinary Institute, Onderstepoort, South Africa;3. Department of Conservation Ecology and Entomology, Stellenbosch University, Matieland, South Africa;1. Center for Global Health and Diseases, Case Western Reserve University, Biomedical Research Building, 2109 Adelbert Rd., Cleveland, OH 44106, USA;2. University of New Mexico, Department of Anthropology, Albuquerque, 1 University of New Mexico, NM 87131, USA;3. Bahiana School of Medicine and Public Health, Av. Silveira Martins, n° 3386, Salvador, Bahia 41150-100, Brazil;4. Gonçalo Moniz Research Centre, Oswaldo Cruz Foundation, Rua Waldemar Falcão, 121 Brotas, Salvador, Bahia 40296-710, Brazil;5. School of Medicine, Federal University of Bahia, Salvador, Bahia, Brazil;6. Yale School of Public Health, Yale University, New Haven, CT, USA;7. Department of Tropical Medicine, Tulane School of Public Health and Tropical Medicine, Tidewater Building, 1440 Canal Street, New Orleans, LA 70112, USA;1. Institute for Stem Cell Biology and Regenerative Medicine, Stanford University School of Medicine, Stanford, CA, USA;2. Laboratory of Experimental Pathology, Gonçalo Moniz Institute, Oswaldo Cruz Foundation, Salvador, BA, Brazil;3. Physiology, School of Medicine, National University of Ireland, Galway, Galway, Ireland;4. Vila Velha University, Vila Velha, ES, Brazil;1. Centro de Parasitologia e Micologia, Instituto Adolfo Lutz, Sao Paulo, Brazil;2. Nucleo de Microscopia Eletrônica, Instituto Adolfo Lutz, Sao Paulo, Brazil;3. Laboratório Regional de Sorocaba, Instituto Adolfo Lutz, Sao Paulo, Brazil;4. Centro de Patologia, Instituto Adolfo Lutz, Sao Paulo, Brazil;5. Departamento de Morfologia e Patologia, Faculdade de Ciências Médicas e Saúde, Pontifícia Universidade Católica, São Paulo, Brazil |
| |
| Abstract: | Short-term prognosis of advanced schistosomiasis has not been well studied. We aimed to construct prognostic models using machine learning algorithms and to identify the most important predictors by utilising routinely available data under the government medical assistance programme. An established database of advanced schistosomiasis in Hunan, China was utilised for analysis. A total of 9541 patients for the period from January 2008 to December 2018 were enrolled in this study. Candidate predictors were selected from demographics, clinical features, medical examinations and test results. We applied five machine learning algorithms to construct 1 year prognostic models: logistic regression (LR), decision tree (DT), random forest (RF), artificial neural network (ANN) and extreme gradient boosting (XGBoost). An area under the receiver operating characteristic curve (AUC) was used to evaluate the model performance. The important predictors of the optimal model for unfavourable prognosis within 1 year were identified and ranked. There were 1249 (13.1%) cases having unfavourable prognoses within 1 year of discharge. The mean age of all participants was 61.94 years, of whom 70.9% were male. In general, XGBoost showed the best predictive performance with the highest AUC (0.846; 95% confidence interval (CI): 0.821, 0.871), compared with LR (0.798; 95% CI: 0.770, 0.827), DT (0.766; 95% CI: 0.733, 0.800), RF (0.823; 95% CI: 0.796, 0.851), and ANN (0.806; 95% CI: 0.778, 0.835). Five most important predictors identified by XGBoost were ascitic fluid volume, haemoglobin (HB), total bilirubin (TB), albumin (ALB), and platelets (PT). We proposed XGBoost as the best algorithm for the evaluation of a 1 year prognosis of advanced schistosomiasis. It is considered to be a simple and useful tool for the short-term prediction of an unfavourable prognosis for advanced schistosomiasis in clinical settings. |
| |
| Keywords: | Advanced schistosomiasis Prognosis Machine learning XGBoost |
| 本文献已被 ScienceDirect 等数据库收录！ |
|
