为营养不良分析设计信息系统 Apriori 算法

Indri Sulistianingsih, Wirda Fitriani, Darmeli Nasution
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引用次数: 0

摘要

营养不良会对个人和社区福祉造成持久损害,需要采取以数据为依据的行动。通过信息系统进行高级分析,为从多维健康数据中发现启示性模式提供了途径。本文概述了通过 Apriori 算法挖掘营养不良数据集的系统设计,包括预处理、建模、分析和解释技术。核心数据挖掘方法可提取连接营养状况参数和食物摄入模式的频率、关联和预测规则。在专家评估之前,定制算法会通过统计方法筛选出高信度的关联结果。系统测试验证了准确的结构,以揭示村一级的营养不良膳食风险因素。健康洞察力的系统化和计算增强为基于需求的分析平台提供了模板。通过对社区数据进行有针对性的分析,可以采取有影响力的干预措施。以数据挖掘为核心的定制信息系统的潜力与需要跨学科推动的领域挑战一起得到了强调。内嵌 Apriori 管道的 "数据到决策 "系统展示了应用信息学通过揭示错综复杂的公共福利数据中可操作的模式来改变营养不良战略。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Design Information Systems for Malnutrition Analysis Apriori Algorithm
Malnutrition drives lasting detriments across individual and community wellbeing, requiring data-informed action. Advanced analytics through information systems present pathways for revelatory pattern detection from multidimensional health data. This paper outlines a system design encompassing preprocessing, modeling, analysis and interpretation techniques for mining malnutrition dataset through Apriori algorithm. The core data mining methodology enables extraction of frequencies, associations and prediction rules linking nutritional status parameters and food intake patterns. Custom algorithms filter results to high-confidence associations via statistical measures before expert evaluation. System testing verifies accurate architecture for surfaced dietary risk factors of malnutrition down to village-level. The systemization and computational augmentation of health insight derivation provides a template for needs-based analytics platforms. By targeting analysis to community data, impactful interventions become possible. The potential of customized information systems with data mining at the core is highlighted alongside domain challenges requiring cross-disciplinary impetus. The data-to-decisions system with embedded Apriori pipelines demonstrates applied informatics transforming malnutrition strategy through unveiling actionable patterns within intricacies of public welfare data.
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