论文标题
使用机器学习和深层神经网络诊断心脏病的新方法
A Novel Approach to the Diagnosis of Heart Disease using Machine Learning and Deep Neural Networks
论文作者
论文摘要
心脏病是全球死亡的主要原因。目前，有33％的病例被误诊了，大约一半的心肌梗死发生在不预计处于危险中的人中。人工智能的使用可能会减少错误的机会，从而导致可能的较早诊断，这可能是某些人的生与死之间的区别。该项目的目的是使用机器学习（ML）和深神经网络（DNN）算法制定辅助心脏病诊断的应用。该数据集是从克利夫兰诊所基金会（Cleveland Clinic Foundation）提供的，模型是根据各种优化和超级参数化技术（包括网格搜索算法）构建的。使用DNN开发了在烧瓶上运行并使用引导程序的应用程序，因为它的性能高于随机森林ML模型，总准确率为92％。
Heart disease is the leading cause of death worldwide. Currently, 33% of cases are misdiagnosed, and approximately half of myocardial infarctions occur in people who are not predicted to be at risk. The use of Artificial Intelligence could reduce the chance of error, leading to possible earlier diagnoses, which could be the difference between life and death for some. The objective of this project was to develop an application for assisted heart disease diagnosis using Machine Learning (ML) and Deep Neural Network (DNN) algorithms. The dataset was provided from the Cleveland Clinic Foundation, and the models were built based on various optimization and hyper parametrization techniques including a Grid Search algorithm. The application, running on Flask, and utilizing Bootstrap was developed using the DNN, as it performed higher than the Random Forest ML model with a total accuracy rate of 92%.
