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使用混合的AITH2O算法和SANFIS分类器进行心脏病预测的新方法
Jayachitra Sekar1, Prasanth Aruchamy2
1Department of Electronics and Communication Engineering, PSNA College of Engineering and Technology, Dindigul, India.
Network (Bristol, England)
|September 25, 2024
概括
这项研究引入了一种新的混合优化算法 (AITH2O),以提高心脏病预测的准确性. 新方法显著提高了智能医疗保健应用中的诊断性能.
科学领域:
- 心脏病学 心脏病学
- 人工智能的人工智能
- 机器学习 机器学习
背景情况:
- 心脏病对全球健康构成重大威胁,导致高死亡率和发病率.
- 准确的心脏病早期预测对于有效的患者治疗和医疗建议至关重要.
- 现有的机器学习模型面临着计算复杂性和过度装配的挑战,限制了诊断准确性.
研究的目的:
- 开发一种新的混合优化算法,以提高心脏病分类准确度.
- 在智能医疗保健应用中优化计算时间,用于心脏病预测.
- 解决现有的机器学习分类器在准确性和效率方面的局限性.
主要方法:
- 使用混合算术优化和基于双胞胎突变的哈里斯霍克优化 (AITH2O) 算法进行特征选择.
- 稳定自适应神经模糊推理系统 (SANFIS) 分类器的参数调整与AITH2O算法.
- 使用克利夫兰心脏病数据集和模拟在MATLAB 2020a.中的验证.
主要成果:
- 拟议的混合AITH2O算法展示了强大的勘探和开发能力,并实现了更快的融合.
- 混合SANFIS分类器在心脏病预测方面实现了99.28%的卓越准确性.
- 真正阳性率达到了99.46%,超过了现有的最先进的技术.
结论:
- 开发的混合优化算法 (AITH2O) 有效地提高了心脏病预测的准确性和效率.
- 由AITH2O优化的稳定自适应神经模糊推理系统 (SANFIS) 分类器为智能医疗诊断提供了一个有前途的解决方案.
- 这种方法为早期发现和治疗心脏病提供了强大而准确的方法.
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