Bilal Anas, Alzahrani Abdulkareem, Almohammadi Khalid, Saleem Muhammad, Farooq Muhammad Sajid, Sarwar Raheem
College of Information Science and Technology, Hainan Normal University, Haikou, China.
Department of Computer Science, Faculty of Computing and Information, Al-Baha University, Al-Baha, Saudi Arabia.
Front Med (Lausanne). 2025 Jul 9;12:1596335. doi: 10.3389/fmed.2025.1596335. eCollection 2025.
Cardiovascular diseases (CVDs) are complex and affect a large part of the world's population; early accurate and timely prediction is also complicated. Typically, predicting CVDs involves using statistical models and other forms of standard machine learning. Although these methods offer some level of prediction, their black-box nature severely hinders the ability of the healthcare professional to trust and use the predictions. The following are some of the challenges that Explainable Artificial Intelligence (XAI) may solve since it can give an understanding of the decision-making system of AI to build confidence and increase usability.
This research introduced an intelligent forecasting system for cardiovascular events using XAI and addressed the limitations of traditional methods. This proposed system incorporates advanced machine learning algorithms integrated with XAI to examine a dataset comprising 308,737 patient records with features including age, BMI, blood pressure, cholesterol levels, and lifestyle factors. This dataset was sourced from the Kaggle Cardiovascular Disease dataset.
Incorporating XAI offers an understandable explanation so that the healthcare professional can understand and make the AI-driven prediction trustworthy enough to improve the decision-making of treatment and care delivery for the patients. The simulation results of the proposed system provide better results than those of the previously published research works in terms of 91.94% accuracy and 8.06% miss rate.
This proposed system makes it clear that XAI has the potential to significantly improve cardiovascular healthcare by enhancing transparency, reliability, and the quality of patient care.
心血管疾病（CVDs）很复杂，影响着世界上很大一部分人口；早期准确及时的预测也很复杂。通常，预测心血管疾病涉及使用统计模型和其他形式的标准机器学习。尽管这些方法提供了一定程度的预测，但它们的黑箱性质严重阻碍了医疗保健专业人员信任和使用这些预测的能力。以下是可解释人工智能（XAI）可能解决的一些挑战，因为它可以让人们了解人工智能的决策系统，从而建立信心并提高可用性。
本研究引入了一种使用XAI的心血管事件智能预测系统，并解决了传统方法的局限性。该提议的系统结合了与XAI集成的先进机器学习算法，以检查一个包含308737条患者记录的数据集，这些记录的特征包括年龄、体重指数、血压、胆固醇水平和生活方式因素。这个数据集来自Kaggle心血管疾病数据集。
纳入XAI提供了一个可理解的解释，以便医疗保健专业人员能够理解并使人工智能驱动的预测足够可信，从而改善对患者的治疗和护理决策。该提议系统的模拟结果在准确率为91.94%和漏报率为8.06%方面比之前发表的研究工作提供了更好的结果。
该提议的系统表明，XAI有潜力通过提高透明度、可靠性和患者护理质量来显著改善心血管医疗保健。
