Thomas Anvin, Jose Rejath, Syed Faiz, Wei Ong Chi, Toma Milan
College of Osteopathic Medicine, New York Institute of Technology, Old Westbury, NY, USA.
School of Chemistry, Chemical Engineering, and Biotechnology, Nanyang Technological University, Singapore.
Technol Health Care. 2024;32(5):3535-3556. doi: 10.3233/THC-240582.
Cardiovascular diseases remain a leading cause of global morbidity and mortality, with heart attacks and strokes representing significant health challenges. The accurate, early diagnosis and management of these conditions are paramount in improving patient outcomes. The specific disease, cardiovascular occlusions, has been chosen for the study due to the significant impact it has on public health. Cardiovascular diseases are a leading cause of mortality globally, and occlusions, which are blockages in the blood vessels, are a critical factor contributing to these conditions.
By focusing on cardiovascular occlusions, the study aims to leverage machine learning to improve the prediction and management of these events, potentially helping to reduce the incidence of heart attacks, strokes, and other related health issues. The use of machine learning in this context offers the promise of developing more accurate and timely interventions, thus improving patient outcomes.
We analyze diverse datasets to assess the efficacy of various machine learning algorithms in predicting heart attacks and strokes, comparing their performance to pinpoint the most accurate and reliable models. Additionally, we classify individuals by their predicted risk levels and examine key features that correlate with the incidence of cardiovascular events. The PyCaret machine learning library's Classification Module was key in developing predictive models which were evaluated with stratified cross-validation for reliable performance estimates.
Our findings suggest that machine learning can significantly improve the prediction accuracy for heart attacks and strokes, facilitating earlier and more precise interventions. We also discuss the integration of machine learning models into clinical practice, addressing potential challenges and the need for healthcare professionals to interpret and apply these predictions effectively.
The use of machine learning for risk stratification and the identification of modifiable factors may empower preemptive approaches to cardiovascular care, ultimately aiming to reduce the occurrence of life-threatening events and improve long-term patient health trajectories.
心血管疾病仍然是全球发病和死亡的主要原因，心脏病发作和中风是重大的健康挑战。准确、早期诊断和管理这些疾病对于改善患者预后至关重要。由于心血管阻塞对公众健康有重大影响，因此选择了这种特定疾病进行研究。心血管疾病是全球死亡的主要原因，而血管阻塞是导致这些疾病的关键因素。
通过关注心血管阻塞，本研究旨在利用机器学习改善对这些事件的预测和管理，有可能帮助降低心脏病发作、中风和其他相关健康问题的发生率。在这种情况下使用机器学习有望开发出更准确、及时的干预措施，从而改善患者预后。
我们分析各种数据集，以评估各种机器学习算法在预测心脏病发作和中风方面的功效，比较它们的性能以找出最准确、可靠的模型。此外，我们根据预测的风险水平对个体进行分类，并检查与心血管事件发生率相关的关键特征。PyCaret机器学习库的分类模块是开发预测模型的关键，这些模型通过分层交叉验证进行评估以获得可靠的性能估计。
我们的研究结果表明，机器学习可以显著提高心脏病发作和中风的预测准确性，促进更早、更精确的干预。我们还讨论了将机器学习模型整合到临床实践中的问题，解决潜在挑战以及医疗保健专业人员有效解释和应用这些预测的必要性。
使用机器学习进行风险分层和识别可改变因素可能会使心血管护理采取先发制人的方法，最终目标是减少危及生命事件的发生并改善患者的长期健康轨迹。
