A Prediction Technique for Heart Disease Based on Long Short Term Memory Recurrent Neural Network

In recent years, heart disease is one of the leading cause of death for both women and men. So, heart disease prediction is considered as a significant part in the clinical data analysis. Standard data mining techniques like Support Vector Machine (SVM), Naïve Bayes and other machine learning techniques used in the earlier research for heart disease prediction. These methods are not sufficient for effective heart disease prediction due to insufficient test data. In this research, Bi-directional Long Short Term Memory with Conditional Random Field (BiLSTM-CRF) has been proposed to increase the efficiency of heart disease prediction. The input medical data were analyzed in a bidirectional manner for effective analysis, and CRF provided the linear relationship between the features. The BiLSTMCRF method has been tested on the Cleveland dataset to analyze the performance and compared with existing methods. The results showed that the proposed BiLSTM-CRF outperformed the existing methods in heart disease prediction. The average accuracy of the proposed BiLSTM-CRF is 90.04%, which is higher than the existing methods.

[1]  Chee Peng Lim,et al.  A hybrid intelligent system for medical data classification , 2014, Expert Syst. Appl..

[2]  Yisheng Lv,et al.  Data driven parallel prediction of building energy consumption using generative adversarial nets , 2019, Energy and Buildings.

[3]  Shuo Yang,et al.  An improved Id3 algorithm for medical data classification , 2017, Comput. Electr. Eng..

[4]  Tao Chen,et al.  Expert Systems With Applications , 2022 .

[5]  Yi-Ping Phoebe Chen,et al.  Computational intelligence for heart disease diagnosis: A medical knowledge driven approach , 2013, Expert Syst. Appl..

[6]  Saeid Nahavandi,et al.  Medical data classification using interval type-2 fuzzy logic system and wavelets , 2015, Appl. Soft Comput..

[7]  Mohammad Sohel Rahman,et al.  A Random Forest based predictor for medical data classification using feature ranking , 2019, Informatics in Medicine Unlocked.

[8]  Kasturi Dewi Varathan,et al.  Identification of significant features and data mining techniques in predicting heart disease , 2019, Telematics Informatics.

[9]  Hilal Kaya,et al.  A new framework using deep auto-encoder and energy spectral density for medical waveform data classification and processing , 2019, Biocybernetics and Biomedical Engineering.

[10]  Jingfa Li,et al.  Data mining new energy materials from structure databases , 2019, Renewable and Sustainable Energy Reviews.

[11]  I. Vlahavas,et al.  Machine Learning and Data Mining Methods in Diabetes Research , 2017, Computational and structural biotechnology journal.

[12]  Leonardo Juan Ramírez López,et al.  A novel heart rate attractor for the prediction of cardiovascular disease , 2019, Informatics in Medicine Unlocked.

[13]  C. Beulah Christalin Latha,et al.  Improving the accuracy of prediction of heart disease risk based on ensemble classification techniques , 2019, Informatics in Medicine Unlocked.

[14]  Dayou Liu,et al.  Evolving support vector machines using fruit fly optimization for medical data classification , 2016, Knowl. Based Syst..

[15]  Yan Tian,et al.  LSTM-based traffic flow prediction with missing data , 2018, Neurocomputing.

[16]  Gautam Srivastava,et al.  Effective Heart Disease Prediction Using Hybrid Machine Learning Techniques , 2019, IEEE Access.