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Various Preprocessing Methods for Neural Network Based Heart Disease Prediction

  • Kavita Burse
  • Vishnu Pratap Singh Kirar
  • Abhishek Burse
  • Rashmi BurseEmail author
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 851)

Abstract

Medical diagnosis focuses on previous knowledge and behavior of the disease. Sometimes it is very difficult for a doctor to predict the disease accurately and fast, based on his knowledge and experience. With the development of machine learning algorithms diagnosis solutions can be developed for many personalized medical problems. Artificial Neural Network (ANN) is going to become an essential part of medical diagnosis. ANN provides self-learning mechanism for complex problems like medical diagnosis. In this research, we are going to propose a novel Multi-Layer Pi-Sigma Neuron Model (MLPSNM) for medical diagnosis. This MLPSNM can diagnose different medical conditions. Proposed MLPSNM is trained by using standard BP algorithm. The bipolar sigmoidal function is used as an activation function. Normalization, PCA, and LDA preprocessing is used for data preprocessing. SVM model with LDA is also proposed in this research. For testing of proposed MLPSNM we select the UCI machine learning datasets.

Keywords

Heart disease Artificial neural network Preprocessing PCA LDA 

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Copyright information

© Springer Nature Singapore Pte Ltd. 2019

Authors and Affiliations

  • Kavita Burse
    • 1
  • Vishnu Pratap Singh Kirar
    • 2
  • Abhishek Burse
    • 3
  • Rashmi Burse
    • 4
    Email author
  1. 1.Department of Electronics & CommunicationTechnocrats Institute of TechnologyBhopalIndia
  2. 2.Department of Computer ScienceUniversity of BedfordshireLutonUK
  3. 3.Department of Computer ScienceOriental Institute of Science and TechnologyBhopalIndia
  4. 4.Department of Computer ScienceMaulana Azad National Institute of TechnologyBhopalIndia

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