Abdullah Monir
Department of Computer Science and Artificial Intelligence, College of Computing and Information Technology, University of Bisha, Bisha, Saudi Arabia.
Front Artif Intell. 2025 Jan 10;7:1539588. doi: 10.3389/frai.2024.1539588. eCollection 2024.
Cardiac disease refers to diseases that affect the heart such as coronary artery diseases, arrhythmia and heart defects and is amongst the most difficult health conditions known to humanity. According to the WHO, heart disease is the foremost cause of mortality worldwide, causing an estimated 17.8 million deaths every year it consumes a significant amount of time as well as effort to figure out what is causing this, especially for medical specialists and doctors. Manual methods for detecting cardiac disease are biased and subject to medical specialist variance. In this aspect, machine learning algorithms have proved to be effective and dependable alternatives for detecting and classifying patients who are affected by heart disease. Precise and prompt detection of human heart disease can assist in avoiding heart failure within the initial stages and enhance patient survival. This study proposed a novel Enhanced Multilayer Perceptron (EMLP) framework complemented by data refinement techniques to enhance predictive accuracy. The classification model asses using the CDC cardiac disease dataset and achieved 92% accuracy by surpassing all the traditional methods. The proposed framework demonstrates significant potential for the early detection and prediction of cardiac-related diseases. Experimental results indicate that the Enhanced Multilayer Perceptron (EMLP) model outperformed the other algorithms in terms of accuracy, precision, F1-score, and recall, underscoring its efficacy in cardiac disease detection.
心脏病是指影响心脏的疾病，如冠状动脉疾病、心律失常和心脏缺陷，是人类已知的最棘手的健康问题之一。据世界卫生组织称，心脏病是全球首要死因，每年估计导致1780万人死亡。找出病因需要耗费大量时间和精力，尤其是对医学专家和医生而言。检测心脏病的人工方法存在偏差，且因医学专家的不同而有所差异。在这方面，机器学习算法已被证明是检测和分类心脏病患者的有效且可靠的替代方法。准确及时地检测出人类心脏病有助于在早期避免心力衰竭并提高患者生存率。本研究提出了一种新颖的增强多层感知器（EMLP）框架，并辅以数据细化技术以提高预测准确性。该分类模型使用美国疾病控制与预防中心（CDC）的心脏病数据集进行评估，通过超越所有传统方法达到了92%的准确率。所提出的框架在心脏病的早期检测和预测方面显示出巨大潜力。实验结果表明，增强多层感知器（EMLP）模型在准确率、精确率、F1分数和召回率方面优于其他算法，突出了其在心脏病检测中的有效性。
