机器学习模型的开发,以预测老年印尼人的医疗保险索赔成本:一个回顾性预测建模研究
Yeni Mahwati1, Dhihram Tenrisau2, Syarif Rahman Hasibuan3
1Sekolah Tinggi Ilmu Kesehatan Dharma Husada, Bandung, Indonesia.
概括
机器学习模型准确地预测了老年人的印尼医疗保险索赔成本. XGBoost的表现优于其他方法,将门诊护理和住院时间确定为关键成本预测指标.
科学领域:
- 卫生经济学 卫生经济学
- 机器学习在医疗保健中的应用
- 公共卫生信息学 公共卫生信息学
背景情况:
- 印度尼西亚的国民健康保险 (JKN) 计划在管理医疗保健成本方面面临挑战,特别是对其老龄化人口.
- 准确预测医疗保险索赔成本对于JKN的财务可持续性和资源配置至关重要.
- 老年人往往有复杂的医疗保健需求,导致更高和更可变的索赔成本.
研究的目的:
- 开发和评估机器学习模型,用于预测印尼老年人个人医疗保险索赔成本.
- 在这个人口群体中确定影响医疗保险索赔成本的关键因素.
- 为优化JKN的财务管理和服务提供提供见解.
主要方法:
- 利用了印度尼西亚国家健康保险 (JKN) 计划 (2017-2023) 的二次数据.
- 开发并比较了三个预测模型:线性回归,随机森林和XGBoost.
- 使用RMSE,R2和MAE评估模型性能;进行变量重要性分析.
主要成果:
- 与线性回归和随机森林相比,XGBoost表现出更高的性能 (RMSE: 11,360,283; R2: 0.81).
- 门诊护理成为所有模型中索赔成本最重要的预测因素.
- 其他关键预测因素包括停留时间,诊断类型,设施类型和疾病严重程度.
结论:
- XGBoost模型提供了对印尼老年人医疗保险索赔成本的可靠预测.
- 调查结果强调了临床,利用和结构因素在驱动成本方面的重要性.
- 结果可以为JKN提供有针对性的干预,慢性疾病管理和可持续的融资策略.
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