Multi-Branching Neural Network for Myocardial Infarction Prediction

Abstract

Myocardial infarction (MI), also known as heart attack, is the leading cause of death in the United States. Accurate MI prediction is of critical importance to reduce healthcare costs and save lives. Rapid developments in healthcare data infrastructure and information technology provide an unprecedented opportunity for data-driven MI prediction. However, real-world medical data are generally subject to a high level of uncertainty with imbalanced issue and considerable missing values, which pose significant challenges for reliable disease prediction. Realizing the full potential of medical data calls upon the development of novel machine learning methods that are capable of handling the uncertainty factors in medical data. In this paper, we propose a Multi-Branching Neural Network (MB-NN) framework for robust and reliable MI prediction. First, we implement the weighted K-Nearest Neighbors (wKNN) method to estimate the missing values in the medical data. Second, we develop a Hierarchical Clustering (HC)-based under-sampling approach to create multiple balanced sub-datasets from the original imbalanced data to eliminate the potential bias caused by imbalanced data distribution in model training. Third, we combine a multi-branching architecture with multi-layer perceptron (MLP) to further handle the imbalanced data for robust MI prediction. We evaluate the proposed MB-NN framework on the medical records from the Cohort Component of the Atherosclerosis Risk in Communities (ARIC). Experimental results show that the MB-NN method achieves better performance in MI prediction compared with existing widely used machine learning methods.