SPATIAL DISTRIBUTION AND MACHINE LEARNING PREDICTION OF SEXUALLY TRANSMITTED INFECTIONS AND ASSOCIATED FACTORS AMONG SEXUALLY ACTIVE MEN AND WOMEN IN ETHIOPIA, EVIDENCE FROM EDHS 2016

Abstract

ABSTRACT Introduction; Sexually Transmitted Infections Objective: Objective of this study is to investigate spatial distribution and machine learning prediction of Sexually Transmitted Infection and associated factors among sexually active Men and Women in Ethiopia, Evidence from EDHS 2016. Methods: A community-based cross-sectional study which was conducted from January 18, 2016, to June 27, 2016. The total sample size for spatial analysis after weighting EDHS data was 20,740. The analysis was done using spatial autocorrelation Moran’s I to detect a cluster of sexually transmitted infection. Spatial scan statics was done to identify local significant clusters based on the Bernoulli model using the SaTScan™ version 9.6. Supervised machine learning model such as C5.0 Decision tree, Random Forest, Support Vector Machine, Naïve Bayes and Logistic regression was applied on 2016 EDHS dataset of 20,799 unweighted records and analyzed their performance. Association rules were done using unsupervised machine learning algorithm. Result: The spatial distribution of STI in Ethiopia was clustered across the country with a global Moran’s index=0.06 and p value=0.04. Random Forest algorithm was best for STI prediction with 69.48% balanced accuracy and 68.50% area under the curve. Random forest model showed that region, wealth, age category, educational level, age at first sex, working status, marital status, media access, alcohol drinking, chat chewing and sex of the respondent were the top 11 predictor of STI in Ethiopia. Key words: STI, spatial distribution, machine learning prediction, Ethiopia.