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Correlation analysis of demographic factors on low birth weight and prediction modeling using machine learning techniques

机译：低出生体重人口统计学因素的相关性分析及基于机器学习技术的预测模型

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According to a national survey in Bangladesh, a south Asian country, approximately 22.6 percent of the new born babies are born with low birth weight (below 2.5 kg or 2500 grams) [13]. There are some key factors regarding low birth weight which are clinically recognized but apart from the clincical perspective some other health and demographic factors also play a vital role in this phenomenon which can be directly or indirectly associated. The purpose of this study is to utilize the potential machine learning algorithms to construct a predictive model for low birth weight given some health and demographic data related to neonatal health condition in the context of Bangladesh. For the predictive analysis, algorithms like Logistic Regression, Naïve Bayes, Random Forest, K-Nearest Neighbor, Support Vector Machine, Neural Network models have been used in the study. The findings of this study can be a guideline for the health professionals as well as the researchers for analyzing low birth weight infants which can help the people in mass to understand and take necessary precautions to avoid of any such event where a child is born with weight less than the average.
机译：根据南亚国家孟加拉国的一项全国调查，大约22.6％的新生婴儿出生时体重很轻（2.5公斤或2500克以下）[13]。有一些关于低出生体重的关键因素在临床上得到公认，但是除了临床观点之外，其他一些健康和人口统计学因素在这种现象中也起着至关重要的作用，这些因素可以直接或间接相关。这项研究的目的是利用潜在的机器学习算法，为孟加拉国提供一些与新生儿健康状况相关的健康和人口统计数据，从而为低出生体重建立预测模型。为了进行预测分析，研究中使用了诸如Logistic回归，朴素贝叶斯，随机森林，K最近邻，支持向量机，神经网络模型之类的算法。这项研究的结果可以为卫生专业人员以及研究人员分析低出生体重的婴儿提供指导，这可以帮助大众了解并采取必要的预防措施，避免发生儿童出生时体重过重的任何情况低于平均水平。

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