Abstract：
BACKGROUND: The second most common cause of death for children under five is diarrhea. Early Predicting diarrhea disease and identify its determinants (factors) using an advanced machine learning model is the most effective way to save the lives of children. Hence, this study aimed to predict diarrheal diseases, identify their determinants, and generate some rules using machine learning models.
METHODS: The study used secondary data from the 12 east African countries for DHS dataset analysis using Python. Machine learning techniques such as Random Forest, Decision Tree (DT), K-Nearest Neighbor, Logistic Regression (LR), wrapper feature selection and SHAP values are used for identify determinants.
RESULTS: The final experimentation results indicated the random forest model performed the best to predict diarrhea disease with an accuracy of 86.5%, precision of 89%, F-measure of 86%, AUC curve of 92%, and recall of 82%. Important predictors\' identified age, countries, wealth status, mother\'s educational status, mother\'s age, source of drinking water, number of under-five children immunization status, media exposure, timing of breast feeding, mother\'s working status, types of toilet, and twin status were associated with a higher predicted probability of diarrhea disease.
CONCLUSIONS: According to this study, child caregivers are fully aware of sanitation and feeding their children, and moms are educated, which can reduce child mortality by diarrhea in children in east Africa. This leads to a recommendation for policy direction to reduce infant mortality in East Africa.
METHODS: The study used secondary data from the 12 east African countries for DHS dataset analysis using Python. Machine learning techniques such as Random Forest, Decision Tree (DT), K-Nearest Neighbor, Logistic Regression (LR), wrapper feature selection and SHAP values are used for identify determinants.
RESULTS: The final experimentation results indicated the random forest model performed the best to predict diarrhea disease with an accuracy of 86.5%, precision of 89%, F-measure of 86%, AUC curve of 92%, and recall of 82%. Important predictors\' identified age, countries, wealth status, mother\'s educational status, mother\'s age, source of drinking water, number of under-five children immunization status, media exposure, timing of breast feeding, mother\'s working status, types of toilet, and twin status were associated with a higher predicted probability of diarrhea disease.
CONCLUSIONS: According to this study, child caregivers are fully aware of sanitation and feeding their children, and moms are educated, which can reduce child mortality by diarrhea in children in east Africa. This leads to a recommendation for policy direction to reduce infant mortality in East Africa.
摘要：
背景：五岁以下儿童死亡的第二大常见原因是腹泻。使用先进的机器学习模型早期预测腹泻疾病并确定其决定因素（因素）是挽救儿童生命的最有效方法。因此，这项研究旨在预测腹泻疾病，确定它们的决定因素，并使用机器学习模型生成一些规则。
方法：该研究使用来自12个东非国家的二级数据，使用Python进行DHS数据集分析。机器学习技术，如随机森林，决策树(DT)K-最近的邻居,逻辑回归(LR)，包装器特征选择和SHAP值用于识别行列式。
结果：最终实验结果表明，随机森林模型对腹泻疾病的预测效果最好，准确率为86.5%，精度为89%，F-测量86%，92%的AUC曲线，召回82%。重要的预测因素\'确定的年龄，国家,财富地位,母亲的教育状况，母亲的年龄，饮用水源,5岁以下儿童免疫接种情况，媒体曝光,母乳喂养的时机,母亲的工作状态，厕所的种类,双胎状态与腹泻疾病的预测概率较高相关。
结论：根据这项研究，儿童照顾者充分意识到卫生和喂养他们的孩子，妈妈们都受过教育，这可以降低东非儿童腹泻的儿童死亡率。这导致建议制定政策方向，以降低东非的婴儿死亡率。
方法：该研究使用来自12个东非国家的二级数据，使用Python进行DHS数据集分析。机器学习技术，如随机森林，决策树(DT)K-最近的邻居,逻辑回归(LR)，包装器特征选择和SHAP值用于识别行列式。
结果：最终实验结果表明，随机森林模型对腹泻疾病的预测效果最好，准确率为86.5%，精度为89%，F-测量86%，92%的AUC曲线，召回82%。重要的预测因素\'确定的年龄，国家,财富地位,母亲的教育状况，母亲的年龄，饮用水源,5岁以下儿童免疫接种情况，媒体曝光,母乳喂养的时机,母亲的工作状态，厕所的种类,双胎状态与腹泻疾病的预测概率较高相关。
结论：根据这项研究，儿童照顾者充分意识到卫生和喂养他们的孩子，妈妈们都受过教育，这可以降低东非儿童腹泻的儿童死亡率。这导致建议制定政策方向，以降低东非的婴儿死亡率。
