딥러닝 기반의 다층 퍼셉트론을 이용한 심장병 예측 연구

Abstract

Deep Learning, an advanced technology for machine learning, is currently being applied to various industrial fields and is being actively applied and developed in the fields of finance, e-commerce, and Internet of Thing, which require prediction. In particular, this study will be applied to the prediction of heart disease in the medical field, and it will provide the advantage of early detection of heart disease or saving medical treatment cost such as angiography for non-heart disease patient. To apply the Multilayer Perceptron machine learning technique for Deep Learning, 2 Hidden Layers and 10 Perceptons can be constructed to improve learning accuracy. By using Back Propagation during learning, it is reversed from the Output Layer to the Hidden Layer so as to reduce the error. The error is reduced by using ReLU or Sigmoid function, and the generated prediction model optimizes the model through the Adam optimization function. In a case study, heart disease data is learned using heart disease diagnosis and presence or absence of heart disease provided by the Machine Learning & Artificial Intelligence System Center at the University of California, Irvine. The Deep Learning module is developed using a Python-based TensorFlow and validated using randomly extracted data samples from heart disease data after learning.