Department of Computer Engineering and Informatics, University of Patras, 26504 Patras, Greece.
Sensors (Basel). 2023 Jan 19;23(3):1161. doi: 10.3390/s23031161.
Cardiovascular diseases (CVDs) are now the leading cause of death, as the quality of life and human habits have changed significantly. CVDs are accompanied by various complications, including all pathological changes involving the heart and/or blood vessels. The list of pathological changes includes hypertension, coronary heart disease, heart failure, angina, myocardial infarction and stroke. Hence, prevention and early diagnosis could limit the onset or progression of the disease. Nowadays, machine learning (ML) techniques have gained a significant role in disease prediction and are an essential tool in medicine. In this study, a supervised ML-based methodology is presented through which we aim to design efficient prediction models for CVD manifestation, highlighting the SMOTE technique's superiority. Detailed analysis and understanding of risk factors are shown to explore their importance and contribution to CVD prediction. These factors are fed as input features to a plethora of ML models, which are trained and tested to identify the most appropriate for our objective under a binary classification problem with a uniform class probability distribution. Various ML models were evaluated after the use or non-use of Synthetic Minority Oversampling Technique (SMOTE), and comparing them in terms of Accuracy, Recall, Precision and an Area Under the Curve (AUC). The experiment results showed that the Stacking ensemble model after SMOTE with 10-fold cross-validation prevailed over the other ones achieving an Accuracy of 87.8%, Recall of 88.3%, Precision of 88% and an AUC equal to 98.2%.
心血管疾病 (CVDs) 现在是导致死亡的主要原因，因为生活质量和人类习惯发生了重大变化。CVDs 伴有各种并发症，包括涉及心脏和/或血管的所有病理变化。病理变化的列表包括高血压、冠心病、心力衰竭、心绞痛、心肌梗死和中风。因此，预防和早期诊断可以限制疾病的发作或进展。如今，机器学习 (ML) 技术在疾病预测中发挥了重要作用，是医学的重要工具。在这项研究中，提出了一种基于监督机器学习的方法，通过该方法，我们旨在设计用于 CVD 表现的高效预测模型，突出 SMOTE 技术的优势。详细分析和了解风险因素，以探索它们对 CVD 预测的重要性和贡献。这些因素作为输入特征提供给众多 ML 模型，这些模型经过训练和测试，以在具有均匀类概率分布的二分类问题下确定最适合我们目标的模型。在使用或不使用 Synthetic Minority Oversampling Technique (SMOTE) 后，对各种 ML 模型进行了评估，并根据准确性、召回率、精度和曲线下面积 (AUC) 对它们进行了比较。实验结果表明，经过 SMOTE 和 10 倍交叉验证的 Stacking 集成模型优于其他模型，达到了 87.8%的准确率、88.3%的召回率、88%的精度和 98.2%的 AUC。
