This study presents a comprehensive analysis of artificial intelligence (AI) techniques to predict healthcare expenditure per capita (pcHCE) in Turkey. Well‐known AI techniques such as random forest (RF), artificial neural network (ANN), multiple linear regression (MLR), support vector regression (SVR), and relevance vector machine (RVM) were used to forecast pcHCE. Twenty‐nine years of historical data from 1990 to 2018 were used in the training and testing phases of the models. Gross domestic product per capita, life expectancy at birth, unemployment rate, crude birth rate, and the number of physicians and hospitals were used as input variables for the analysis. A genetic algorithm‐based feature selection (GAFS) method was applied to all models to select the relevant and optimal feature subset in the prediction of pcHCE. The comparative results showed that the GAFS method improved the overall performance of all base AI models. The hybrid GAFS‐RF model performed best among all AI‐based prediction methods, with a 99.86% correlation of determination (R²) value at the testing stage.

中文翻译：

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使用基于遗传算法的特征选择的人工智能技术估算土耳其的人均医疗保健支出

这项研究对人工智能（AI）技术进行了全面分析，以预测土耳其的人均医疗保健支出（pcHCE）。众所周知的AI技术（例如随机森林（RF），人工神经网络（ANN），多元线性回归（MLR），支持向量回归（SVR）和相关向量机（RVM））用于预测pcHCE。该模型的训练和测试阶段使用了1990年至2018年的29年历史数据。将人均国内生产总值，出生时的预期寿命，失业率，粗出生率以及医生和医院的数量用作分析的输入变量。将基于遗传算法的特征选择（GAFS）方法应用于所有模型，以选择pcHCE预测中的相关和最佳特征子集。比较结果表明，GAFS方法改善了所有基本AI模型的整体性能。混合GAFS‐RF模型在所有基于AI的预测方法中表现最好，确定相关性为99.86％（R ²）值在测试阶段。