Yining Bao1, Nicholas A Medland2, Christopher K Fairley3, Jinrong Wu4, Xianwen Shang5, Eric P F Chow6, Xianglong Xu3, Zongyuan Ge7, Xun Zhuang8, Lei Zhang9. 1. China Australia Joint Research Centre for Infectious Diseases, School of Public Health, Xi'an Jiaotong University Health Science Centre, Xi'an, Shaanxi 710061, People's Republic of China; Department of Epidemiology and Biostatistics, School of Public Health, Nantong University, No.9 Seyuan Road, Chongchuan District, Nantong, Jiangsu 226019, People's Republic of China; Melbourne Sexual Health Centre, Alfred Health, Melbourne, Australia. 2. Melbourne Sexual Health Centre, Alfred Health, Melbourne, Australia; Central Clinical School, Faculty of Medicine, Nursing and Health Sciences, Monash University, Melbourne, VIC, Australia; The Kirby Institute, University of NSW, Sydney, Australia. 3. China Australia Joint Research Centre for Infectious Diseases, School of Public Health, Xi'an Jiaotong University Health Science Centre, Xi'an, Shaanxi 710061, People's Republic of China; Melbourne Sexual Health Centre, Alfred Health, Melbourne, Australia; Central Clinical School, Faculty of Medicine, Nursing and Health Sciences, Monash University, Melbourne, VIC, Australia. 4. Centre for Eye Research Australia; Ophthalmology, Department of Surgery, The University of Melbourne, Melbourne, Australia; Centre for Data Analytics and Cognition, College of Arts, Social Sciences and Commerce, The La Trobe University, Melbourne, Australia. 5. Centre for Eye Research Australia; Ophthalmology, Department of Surgery, The University of Melbourne, Melbourne, Australia. 6. Melbourne Sexual Health Centre, Alfred Health, Melbourne, Australia; Central Clinical School, Faculty of Medicine, Nursing and Health Sciences, Monash University, Melbourne, VIC, Australia; Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, University of Melbourne, Melbourne, VIC, Australia. 7. Monash e-Research Centre, Faculty of Engineering, Airdoc Research, Nvidia AI Technology Research Centre, Monash University, Melbourne, VIC, Australia. 8. Department of Epidemiology and Biostatistics, School of Public Health, Nantong University, No.9 Seyuan Road, Chongchuan District, Nantong, Jiangsu 226019, People's Republic of China. Electronic address: xzhuang@ntu.edu.cn. 9. China Australia Joint Research Centre for Infectious Diseases, School of Public Health, Xi'an Jiaotong University Health Science Centre, Xi'an, Shaanxi 710061, People's Republic of China; Melbourne Sexual Health Centre, Alfred Health, Melbourne, Australia; Central Clinical School, Faculty of Medicine, Nursing and Health Sciences, Monash University, Melbourne, VIC, Australia; Department of Epidemiology and Biostatistics, College of Public Health, Zhengzhou University, Zhengzhou 450001, Henan, China. Electronic address: Lei.zhang1@xjtu.edu.cn.
Abstract
OBJECTIVES: We aimed to develop machine learning models and evaluate their performance in predicting HIV and sexually transmitted infections (STIs) diagnosis based on a cohort of Australian men who have sex with men (MSM). METHODS: We collected clinical records of 21,273 Australian MSM during 2011-2017. We compared accuracies for predicting HIV and STIs (syphilis, gonorrhoea, chlamydia) diagnosis using four machine learning approaches against a multivariable logistic regression (MLR) model. RESULTS: Machine learning approaches consistently outperformed MLR. Gradient boosting machine (GBM) achieved the highest area under the receiver operator characteristic curve for HIV (76.3%) and STIs (syphilis, 85.8%; gonorrhoea, 75.5%; chlamydia, 68.0%), followed by extreme gradient boosting (71.1%, 82.2%, 70.3%, 66.4%), random forest (72.0%, 81.9%, 67.2%, 64.3%), deep learning (75.8%, 81.0%, 67.5%, 65.4%) and MLR (69.8%, 80.1%, 67.2%, 63.2%). GBM models demonstrated the ten greatest predictors collectively explained 62.7-73.6% of variations in predicting HIV/STIs. STIs symptoms, past syphilis infection, age, time living in Australia, frequency of condom use with casual male sexual partners during receptive anal sex and the number of casual male sexual partners in the past 12 months were most commonly identified predictors. CONCLUSIONS: Machine learning approaches are advantageous over multivariable logistic regression models in predicting HIV/STIs diagnosis.
OBJECTIVES: We aimed to develop machine learning models and evaluate their performance in predicting HIV and sexually transmitted infections (STIs) diagnosis based on a cohort of Australian men who have sex with men (MSM). METHODS: We collected clinical records of 21,273 Australian MSM during 2011-2017. We compared accuracies for predicting HIV and STIs (syphilis, gonorrhoea, chlamydia) diagnosis using four machine learning approaches against a multivariable logistic regression (MLR) model. RESULTS: Machine learning approaches consistently outperformed MLR. Gradient boosting machine (GBM) achieved the highest area under the receiver operator characteristic curve for HIV (76.3%) and STIs (syphilis, 85.8%; gonorrhoea, 75.5%; chlamydia, 68.0%), followed by extreme gradient boosting (71.1%, 82.2%, 70.3%, 66.4%), random forest (72.0%, 81.9%, 67.2%, 64.3%), deep learning (75.8%, 81.0%, 67.5%, 65.4%) and MLR (69.8%, 80.1%, 67.2%, 63.2%). GBM models demonstrated the ten greatest predictors collectively explained 62.7-73.6% of variations in predicting HIV/STIs. STIs symptoms, past syphilis infection, age, time living in Australia, frequency of condom use with casual male sexual partners during receptive anal sex and the number of casual male sexual partners in the past 12 months were most commonly identified predictors. CONCLUSIONS: Machine learning approaches are advantageous over multivariable logistic regression models in predicting HIV/STIs diagnosis.
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