# Ensemble Machine Learning Model to Predict the Waterborne Syndrome
## 2 Related Work  
[PERSON] and [PERSON] [11] used the C4.5 Decision Tree and Nave Bayes algorithms to detect diarrhea fatality rates in Indonesia. The experiment was carried out on a dataset of 231 unique patient records from a diarrhea outbreak that occurred between 2008 and 2014. The study aimed to identify numerous factors that contribute to increased diarrhea occurrences. These include the percentages of poor people, the ratio of health centers per population, the percentage of shelter and sanitation facilities, the facilities for defecating in public and private places, and family behavior. Testing and training data evaluations included conducting confusion matrix, accuracy measurements, and evaluating with Kurva ROC. The study had several limitations. First, limited classification algorithms have been considered while other classification models explicitly studied on diarrhea have not been considered. Secondly, the optimum parameters of the algorithms were not considered. Thirdly, the study data size itself was very limited. These limitations have been explicitly addressed in this study along with considering some of the factors critical in measuring the disease prevalence and management. [PERSON] et al. [12] conducted predictive analysis through multi-classification of infectious diseases, including diarrhea. The study involved employing a deep learning model on the hospital data. The data comprised 20,620 cases of seven types of infectious disease. The authors proposed a new multiple infectious disease diagnostic model (MIDDM), which achieved an accuracy of 99.44% when compared against XGBoost (96.19%), decision tree (90.13%), Bayesian model (85.19%), and logistic regression (91.26%). The study primarily focused on diagnosing infectious diseases assisting doctors who would otherwise have to examine disease based on their personal experience. The research also aimed to stop the spread of infectious diseases in society by alerting the doctors using the proposed model. Unfortunately, the study did not consider the optimal criteria needed for the diagnostic, resulting in a significant difference in the accuracy of the model features. The research was also limited to just one hospital while experimenting with multiple infectious diseases resulting in limited retrospective analysis. The authors of this study [13] employed an artificial neural network (ANN) to analyze data from a Nigerian demographic and health survey to determine the causes of diarrhea among children. This study considered socioeconomic and other daily environmental features such as education levels, water sources, home items, living status, and other urban and rural settings. The experiment's goal was to find the best settings using a five-step search algorithmic approach. According to the findings, the study variables were found to be 95.63% accurate. The findings were also linked to the study's goals for sustainable development. According to the study, 88% of diarrhea fatality rates are caused by a lack of clean water and insufficient sanitization, similar to the experimental results we obtained as part of this study. The inability to connect diarrhea incidence with family members living in the same household was one of the study's weaknesses, which was carefully addressed as part of our investigation. [PERSON] et al. [14] conducted a prediction of diarrhea in the city of Shanghai, China, through comparing across four different models, namely Multiple Linear Regression (MRA), Artificial Neural Network (ANN), Support Vector Regression (SVR), and Random Forest regression (RFR). The authors used [PERSON]'s Ranking correlation analysis for determining the dependent and independent variables. The authors then proposed feed-forward back-propagation NN (BPNN) for training and testing the weights based on the optimal parameters.