How can machine learning predict cholera: insights from experiments and design science for action research

Abstract

Cholera is a leading cause of mortality in Nigeria. The two most significant predictors of cholera are a lack of access to clean water and poor sanitary conditions. Other factors such as natural disasters, illiteracy, and internal conflicts that drive people to seek sanctuary in refugee camps may contribute to the spread of cholera in Nigeria. The aim of this research is to develop a cholera outbreak risk prediction (CORP) model using machine learning tools and data science. In this study, we developed a CORP model using design science perspectives and machine learning to detect cholera outbreaks in Nigeria. Nonnegative matrix factorization (NMF) was used for dimensionality reduction, and synthetic minority oversampling technique (SMOTE) was used for data balancing. Outliers were detected using density-based spatial clustering of applications with noise (DBSCAN) were removed improving the overall performance of the model, and the extreme-gradient boost algorithm was used for prediction. The findings revealed that the CORP model outcomes resulted in the best accuracy of 99.62%, Matthews's correlation coefficient of 0.976, and area under the curve of 99.2%, which were improved compared with the previous findings. The developed model can be helpful to healthcare providers in predicting possible cholera outbreaks. HIGHLIGHTS Identifying the cholera prediction attributes.; Using socioeconomic variables to predict cholera.; The use of NMFSMOTE for outlier detection and balancing of the dataset.; Accuracy improved by the use of NMFSMOTE and DBSCAN.; The developed model can be helpful to healthcare providers in predicting possible cholera outbreaks.;

Keywords

• cholera
• dbscan
• dimensionality reduction
• nmf
• smote and xgboost