基于特征选择和神经网络的糖尿病预测模型研究

采用特征选择算法和人工神经网络建立糖尿病预测模型，阐述模型构建及评价步骤、方法。以灵敏度、特异度、准确率、ROC-AUC为指标评估模型的预测性能，并与其他算法模型进行对比分析，实验结果表明基于特征选择和人工神经网络的糖尿病预测模型对临床指标未知且复杂的数据集具有更好的抗干扰能力和预测性能。

Study on Diabetes Prediction Model Based on Feature Selection and Neural Network

A diabetes prediction model is constructed based on feature selection algorithm and artificial neural network, and the steps and methods of model construction and evaluation are expounded. Sensitivity, specificity, accuracy and ROC-AUC are used as evaluation metrics to evaluate the performance of the proposed model, and the proposed model is compared with other algorithms through experiments. The result indicates that the diabetes prediction model based on feature selection and artificial neural network has better anti-interference capability and predictive performance, which is more suitable for complex datasets with unknown influencing indicators.