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Construct Left Ventricular Hypertrophy Prediction Model Based on Random Forest

  • Jimmy Ming-Tai Wu
  • Meng-Hsiun Tsai
  • Sheng-Han Xiao
  • Tsu-Yang Wu
Conference paper
Part of the Smart Innovation, Systems and Technologies book series (SIST, volume 109)

Abstract

Heart disease ranks second in Taiwan’s top ten cause of death in 2016 and the number of deaths in heart disease increases by about 700 people each year. Left ventricular hypertrophy (LVH) has a significant impact on increasing the morbidity of coronary disease and stroke. Therefore, how to improve the accuracy of heart disease diagnosis is urgent. This study suggests a better method that used K-Nearest Neighbor (KNN) to impute missing values of ECG data and Z-score to standardize ECG data for the requirement of the random forest. This study combined the random forest and ECG data to develop an ECG left ventricular hypertrophy classifier. The experimental results show that the accuracy of the prediction model is 66.1%, the sensitivity is 58%, and the specificity is 70.9%.

Keywords

Electrocardiogram Left ventricular hypertrophy Random forest Machine learning 

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Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Jimmy Ming-Tai Wu
    • 1
  • Meng-Hsiun Tsai
    • 2
  • Sheng-Han Xiao
    • 2
  • Tsu-Yang Wu
    • 1
  1. 1.College of Computer Science and EngineeringShandong University of Science and TechnologyQingdaoChina
  2. 2.Department of Management Information SystemsNational Chung Hsing UniversityTaichungTaiwan

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