撒哈拉以南非洲地区的艾滋病毒预防措施仍未达到联合国艾滋病规划署2014年设定的90-90-90快速目标。确定艾滋病毒状况的预测因子可能有助于有针对性的筛查干预措施,从而改善医疗保健。我们的目标是确定HIV预测因子以及预测感染高危人群。
我们使用机器学习方法,使用基于人群的HIV影响评估(PHIA)数据建立模型,分别来自撒哈拉以南国家的四个国家的41,939名男性和45,105名女性受访者,分别具有30和40个变量。我们在80%的数据上训练和验证了算法,并在剩下的20%的数据上进行了测试,我们在剩下的国家/地区旋转。保留了具有最佳平均f1得分的算法,并对最具预测性的变量进行了训练。我们使用该模型来识别HIV感染者和感染该疾病可能性较高的个体。
与其他五种算法相比,XGBoost算法的应用似乎显着提高了对HIV阳性的识别,男性和女性的f1评分平均值分别为90%和92%。两性的八个最具预测特征是:年龄,与一家之主的关系,最高水平的教育,在那个学校级别的最高年级,为付款而工作,避免怀孕,第一次经历性爱的年龄,财富五分之一。与包含所有变量相比,使用这些变量的模型性能显着提高。我们确定了5名男性和19名女性个体,这些个体需要检测才能找到一名HIV阳性个体。我们还预测,4.14%的男性和10.81%的女性处于高感染风险中。
我们的研究结果为XGBoost算法与社会行为驱动的数据提供了潜在的用途,用于实质性地识别HIV预测因子和预测感染高危个体以进行针对性筛查。
HIV prevention measures in sub-Saharan Africa are still short of attaining the UNAIDS 90-90-90 fast track targets set in 2014. Identifying predictors for HIV status may facilitate targeted screening interventions that improve health care. We aimed at identifying HIV predictors as well as predicting persons at high risk of the infection.
We applied machine learning approaches for building models using population-based HIV Impact Assessment (PHIA) data for 41,939 male and 45,105 female respondents with 30 and 40 variables respectively from four countries in sub-Saharan countries. We trained and validated the algorithms on 80% of the data and tested on the remaining 20% where we rotated around the left-out country. An algorithm with the best mean f1 score was retained and trained on the most predictive variables. We used the model to identify people living with HIV and individuals with a higher likelihood of contracting the disease.
Application of XGBoost algorithm appeared to significantly improve identification of HIV positivity over the other five algorithms by f1 scoring mean of 90% and 92% for males and females respectively. Amongst the eight most predictor features in both sexes were: age, relationship with family head, the highest level of education, highest grade at that school level, work for payment, avoiding pregnancy, age at the first experience of sex, and wealth quintile. Model performance using these variables increased significantly compared to having all the variables included. We identified five males and 19 females individuals that would require testing to find one HIV positive individual. We also predicted that 4·14% of males and 10.81% of females are at high risk of infection.
Our findings provide a potential use of the XGBoost algorithm with socio-behavioural-driven data at substantially identifying HIV predictors and predicting individuals at high risk of infection for targeted screening.