研究婴儿死亡率:基于数据挖掘模型的人口统计分析
Muhammad Islam Satti1, Mir Wajid Ali1, Azeem Irshad2
1Department of Computer Science, Millennium Institute of Technology & Entrepreneurship (MiTE), Karachi, Pakistan.
Open life sciences
|July 24, 2023
概括
儿童死亡率仍然是一个全球性问题,特别是在巴基斯坦和埃塞俄比亚. 这项研究使用数据挖掘来确定关键因素,在预测儿童死亡时达到97.8%的准确性.
科学领域:
- 公共卫生 公共卫生
- 在医疗保健中的数据科学.
- 人口统计数据 人口统计数据
背景情况:
- 五岁以下儿童死亡率仍然是一个重大的全球卫生挑战,特别是在巴基斯坦和埃塞俄比亚等发展中国家.
- 尽管有全球努力,高死亡率仍然存在,需要先进的分析方法来进行有效的干预.
- 预测分析为了解和减轻儿童死亡率趋势提供了一个强大的工具.
研究的目的:
- 通过数据挖掘技术,识别和分类导致巴基斯坦和埃塞俄比亚儿童死亡率的关键因素.
- 根据人口和健康调查数据,开发儿童死亡率的预测模型.
- 突出数据驱动的洞察力对于改善婴儿健康结果的重要性.
主要方法:
- 利用了来自巴基斯坦人口健康调查和埃塞俄比亚人口健康调查的数据集.
- 应用了各种数据挖掘技术,包括贝叶斯网络,J-48 (树),PART (规则诱导),随机森林和多层次感知.
- 评估了多个分类器的性能,以确定儿童死亡率最准确的预测模型.
主要成果:
- 对12,654 (巴基斯坦) 和12,869 (埃塞俄比亚) 记录的分析确定了对儿童死亡率产生影响的关键因素.
- 性能最好的模型在预测儿童死亡频率方面实现了97.8%的平均准确性.
- 开发的模型证明了在研究区域估计五岁以下儿童死亡率的能力.
结论:
- 数据挖掘技术有效地识别了导致巴基斯坦和埃塞俄比亚儿童死亡率的关键因素.
- 已经开发出了一个非常准确的儿童死亡率预测模型,为公共卫生干预提供了有价值的见解.
- 基于这项研究的在线预测工具被推用于帮助医疗保健策略和减少儿童死亡.
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