■随着人工智能技术的出现,机器学习算法已广泛应用于疾病预测领域。
■心血管疾病(CVD)严重危害全球人类健康,因此需要建立有效的CVD预测模型,对控制疾病风险、保障人群身心健康具有重要意义。
■以UCI心脏病数据集为例,最初,构建了单一的机器学习预测模型。随后,六种方法,如皮尔逊,卡方,RFE和LightGBM被综合用于特征筛选。在基本分类器的基础上,进行了软投票融合和堆叠融合,建立了心血管疾病的预测模型,从而实现对高危人群的早期预警和疾病干预。为了解决数据不平衡问题,采用SMOTE方法对数据集进行处理,并利用多维度、多指标对模型的预测效果进行了分析。
■在单分类器模型中,MLP算法对预处理后的心脏病数据集进行了优化。选择功能后,五个特点消除。ENSEM_SV算法结合了基分类器,通过对分类器结果的软投票来确定预测结果,在精度等五个指标上实现了最优值,Jaccard_Score,哈姆_损失,AUC,等。,AUC值达到0.951。RF,ET,GBDT,在由基本分类器组成的第一阶段子模型中采用了LGB算法。选择AB算法作为第二阶段模型,和集成算法ENSEM_ST,通过两个阶段的堆叠融合获得的,在精度等7个指标上表现出最佳性能,灵敏度,F1_Score,Mathew_Corcoef,等。,AUC达到0.952。此外,比较了基于不同训练集占用率的算法分类效果。结果表明,两种融合模型的预测性能均优于单一模型。ENSEM_ST融合的整体效果强于ENSEM_SV融合。
■本研究中建立的融合模型在很大程度上提高了模型的整体分类精度和稳定性。在CVD诊断的预测分析中具有很好的应用价值,为疾病诊断和干预策略提供有价值的参考。
UNASSIGNED: With the advent of artificial intelligence technology, machine learning algorithms have been widely used in the area of disease prediction.
UNASSIGNED: Cardiovascular disease (CVD) seriously jeopardizes human health worldwide, thereby needing the establishment of an effective CVD prediction model that can be of great significance for controlling the risk of the disease and safeguarding the physical and mental health of the population.
UNASSIGNED: Considering the UCI heart disease dataset as an example, initially, a single machine learning prediction model was constructed. Subsequently, six methods such as Pearson, chi-squared, RFE and LightGBM were comprehensively used for the feature screening. On the basis of the base classifiers, Soft Voting fusion and Stacking fusion was carried out to build a prediction model for cardiovascular diseases, in order to realize an early warning and disease intervention for high-risk populations. To address the data imbalance problem, the SMOTE method was adopted to process the data set, and the prediction effect of the model was analyzed using multi-dimensional and multi-indicators.
UNASSIGNED: In the single classifier model, the MLP algorithm performed optimally on the preprocessed heart disease dataset. After feature selection, five features eliminated. The ENSEM_SV algorithm that combines the base classifiers to determine the prediction results by soft voting on the results of the classifiers achieved the optimal value on five metrics such as Accuracy, Jaccard_Score, Hamm_Loss, AUC, etc., and the AUC value reached 0.951. The RF, ET, GBDT, and LGB algorithms were employed in the first stage sub-model composed of base classifiers. The AB algorithm was selected as the second stage model, and the ensemble algorithm ENSEM_ST, obtained by Stacking fusion of the two stages exhibited the best performance on 7 indicators such as Accuracy, Sensitivity, F1_Score, Mathew_Corrcoef, etc., and the AUC reached 0.952. Furthermore, a comparison of the algorithms\' classification effects based on different training set occupancy was carried out. The results indicated that the prediction performance of both the fusion models was better than the single models, and the overall effect of ENSEM_ST fusion was stronger than the ENSEM_SV fusion.
UNASSIGNED: The fusion model established in this study improved the overall classification accuracy and stability of the model to a significant extent. It has a good application value in the predictive analysis of CVD diagnosis, and can provide a valuable reference in the disease diagnosis and intervention strategies.