使用监督机器学习技术识别风险因素和预测食品安全状况

Melaku Alelign, Tesfamariam M Abuhay, Adane Letta, Tizita Dereje
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引用次数: 1

摘要

2018年,全世界登记的营养不良人口超过8.21亿。其中,2.39亿人生活在撒哈拉以南非洲。在埃塞俄比亚、肯尼亚、索马里和南苏丹,这一数字尤其高。埃塞俄比亚粮食不安全的决定因素是多方面的,包括气候变化、国内冲突、自然灾害和社会规范。因此,本研究旨在利用监督式机器学习技术识别埃塞俄比亚西北部家庭层面的风险因素并预测粮食安全状况。为此,从Dabat健康和人口监测中收集了一个数据集,并使用logistic回归在p<0.05的阈值水平上确定了统计上有趣的危险因素。采用随机森林、支持向量机和决策树(C4.5)对农户粮食安全状况进行了预测,并对模型的准确性、精密度、召回率和f1-测度进行了评价。因此,C4.5算法被选为最合适的有监督机器学习算法,召回率为97.23%,准确率为91.58%,f1-measure为80.97%,精度为69.38%。家庭规模、教育水平、户主年龄、通信媒介的数量和类型、牲畜数量、耕地面积、获得信贷的机会和获得灌溉的机会是粮食安全的一些风险因素。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Identifying Risk Factors and Predicting Food Security Status using Supervised Machine Learning Techniques
In 2018, more than 821 million undernourished people were registered all over the world. Of these, 239 million were in Sub-Saharan Africa. The numbers are particularly high in Ethiopia, Kenya, Somalia, and South Sudan. The determinant factors of food insecurity in Ethiopia are multidimensional encompassing climate change, civil conflicts, natural disasters, and social norms. This study, hence, aims to identify risk factors and predict food security status at household level in North West Ethiopia using supervised machine learning techniques. To this end, a dataset was gathered from the Dabat Health and Demographic Surveillance and statistically interesting risk factors were identified using logistics regression at a threshold level of p<0.05. Three experiments were also conducted using random forest, support vector machine and decision tree (C4.5) to predict food security status at household level and the performance of each model was evaluated using accuracy, precision, recall, and f1- measure. As a result, the C4.5 algorithm is selected as the best appropriate supervised machine learning algorithm with 97.23% of recall, 91.58% of accuracy, 80.97% of f1-measure, and 69.38% of precision. Family size, level of education, age of the household head, number and types of communication media, numbers of livestock, cultivated land size, access to credit, and access to irrigation are some of the risk factors of food security.
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