Narasimhan Geetha, Victor Akila
School of Computer Science and Engineering, Vellore Institute of Technology, Vellore, Tamilnadu, India.
Sci Rep. 2025 Mar 31;15(1):10971. doi: 10.1038/s41598-024-73867-x.
Cardiovascular diseases (CVD) a major cause of morbidity and mortality among the world's non-communicable disease incidences. Though these practices are in use for diagnostics of different CVDs in clinical settings, need improvement because they are solving the purpose of only 57% of the patients in emergency. Due to this cost of diagnosis for heart disease is increasing which is the reason for analyzing heart disease and predicting it as early as possible. The main motive of this paper is to find an intelligent method for predicting disease effectively by means of machine learning (ML) and metaheuristic algorithms. Optimization techniques have the merit of handling non-linear complex problems. In this paper, an efficient ML model along with metaheuristic optimization techniques is evaluated for heart disease dataset to enhance the accuracy in predicting the disease. This will help to reduce the death rate due to the severity of heart disease. The SelectKBest feature selection is applied to the Cleveland Heart dataset and overall rank is obtained. Accuracy is measured. The optimization techniques namely Genetic Algorithm Optimized Random Forest (GAORF), Particle Swarm Optimized Random Forest (PSORF), and Ant Colony Optimized Random Forest (ACORF) are applied to the Cleveland dataset. Classification algorithms are performed before and after optimization. The output of the experiment explains that the GAORF performed better for the dataset considered. Also, a comparison is made along with the SelectKBest filter methods. The proposed model achieved better accuracy which is the maximum among other optimization and classification techniques.
心血管疾病（CVD）是全球非传染性疾病发病率中发病和死亡的主要原因。尽管这些方法在临床环境中用于不同心血管疾病的诊断，但仍需改进，因为它们仅能解决57%急诊患者的问题。由于心脏病诊断成本不断增加，因此尽早分析和预测心脏病十分必要。本文的主要目的是通过机器学习（ML）和元启发式算法找到一种有效的疾病预测智能方法。优化技术具有处理非线性复杂问题的优点。本文针对心脏病数据集评估了一种高效的机器学习模型以及元启发式优化技术，以提高疾病预测的准确性。这将有助于降低因心脏病严重程度导致的死亡率。将SelectKBest特征选择应用于克利夫兰心脏数据集并获得总体排名，然后测量准确性。将遗传算法优化随机森林（GAORF）、粒子群优化随机森林（PSORF）和蚁群优化随机森林（ACORF）等优化技术应用于克利夫兰数据集。在优化前后执行分类算法。实验结果表明，对于所考虑的数据集，GAORF表现更好。此外，还与SelectKBest过滤方法进行了比较。所提出的模型取得了更好的准确性，这在其他优化和分类技术中是最高的。
