Multilayer Neural Network Approach in Heart Disease Prediction: Investigating a Framework

  • Authors

    • Sharifah Nurulhikmah Syed Yasin
    • Lily Syahira Sabrina Ab Rahman
    • Nursyafiqah Nadia Hassan
    • Hayati Adilin Mohd Abd Majid
    • Rajeswari Raju
    • Rosmawati Nordin
    2018-12-29
    https://doi.org/10.14419/ijet.v7i4.42.25707
  • multilayer neural network, heart disease, computer-aided detection.

  • The early detection of heart disease is vital to avoid sudden death. There are several symptoms that are familiar to heart disease patients. By analyzing the symptoms and assist with ECG signal's pattern, doctors would be able to confirm the heart disease. Since the conventional method of heart disease detection is quite tedious, the automatic detection is proposed. To automate the early detection, a number of adequate algorithms have been proposed in many studies. The algorithms receive input of patients’ details and generate the predicted result. Therefore, this study is proposed to implement and investigate the performance of multilayer neural network (MNN) approach in producing the heart disease prediction.

     

     

  • References

    1. [1] Wu YC & Feng JW. (2017). Development and application of artificial neural network. Wireless Personal Communications, pp. 1-12.

      [2] Rahim NR, Nordin S, & Dom RM. (2015). Review on barriers and considerations of clinical decision support system for medication prescribing, IEEE Student Conference on Research and Development (SCOReD), pp. 489-494.

      [3] Ahmad F, Mat Isa NA, Hussain Z, Osman MK, & Sulaiman SN. (2015). A GA-based feature selection and parameter optimization of an ANN in diagnosing breast cancer, Pattern Analysis and Applications, 18, pp. 861-870.

      [4] Malaysian Ministry of Health (2016). Prevention of cardiovascular disease in women.

      [5] Mohamed NF, Azan A, Peterson RF, Mohamad Alwi MN, Shaharom MH. (2014). Mental and physical health comparison among psychologically distressed heart failure patients in Malaysia, Procedia - Social and Behavioral Sciences, 127, pp. 412 – 416.

      [6] Malaysian Ministry of Health. (2017). Statistics on Causes of Death, Malaysia.

      [7] Shouman M, Turner T, Stocker R. (2012). Applying k-nearest neighbour in diagnosing heart disease patients. International Journal of Information and Education Technology, 2, pp. 220-223.

      [8] Vafai MH, Ataei M, & Koofigar HR. (2014). Heart disease prediction based on ECG sognal's classification using a genetic-fuzzy system and dynamical model of ECG signals, Biomedical Signal Processing and Control, 14, pp. 291-296.

      [9] Chabchoub S, Mansouri S, & Ben Salah R. (2017). Detection of valvular heart diseases using impedance cardiography ICG, Biocybernetics and Biomedical Engineering, 38(2), pp. 251-261

      [10] Ghwanmeh S, Mohammad A, & Al-Ibrahim A. (2013). Innovative artificial neural networks-based decision support system for heart diseases diagnosis. Journal of Intelligent Learning Systems and Applications, 5, pp. 176-183.

      [11] Subbalakshmi G, Ramesh K, & Rao MC. (2011). Decision Support in heart disease prediction system using naive bayes. Indian Journal of Computer Science and Engineering, 2, pp. 170-176.

      [12] Samuel OW, Asogbon GM, Sangaiah AK, Peng F, & Li G. (2017). An integrated decision support system based on ANN and Fuzzy AHP for heart failure risk prediction. Expert System with Applications, 68, pp. 163-172.

      [13] Kahtan H, Zamli KZ, Fatthi WN, Abdullah A, Abdulleteef M, Kamarulzaman NS. (2018). Heart disease diagnosis system using fuzzy logic, Proceedings of the 2018 7th International Conference on Software and Computer Applications, pp. 297-301

      [14] Awang MK & Siraj F. (2013). Utilization of an artificial neural network in the prediction of heart disease, 5(4), 159-166.

      [15] Arabasadi Z, Alizadehsani R, Roshanzamir M, Moosaei H, Yarifard AA. (2017). Computer aided decision making for heart disease detection using hybrid neural network-Genetic algorithm. Computer Methods and Programs in Biomedicine, 141, pp. 19-26.

      [16] Lichman M. (2013). Heart disease data set. machine learning repository, University of California

      [17] Bhatia S, Prakash P, & Pillai GN (2008). SVM based decision support system for heart disease classification with integer-coded genetic algorithm to select critical features, World Congress on Engineering and Computer Science, pp. 1-5.

      [18] Ismail FS & Bakar NA. (2015). Adaptive mechanism for GA-NN to enhance prediction model, Proceedings of the 9th International Conference on Ubiquitous Information Management and Communication, pp. 1-5.

      [19] Waghulde NP & Patil NP. (2014). Genetic neural approach for heart disease prediction, International Journal of Advanced Computer Research, 4, pp. 778-784.

  • Downloads

  • How to Cite

    Nurulhikmah Syed Yasin, S., Syahira Sabrina Ab Rahman, L., Nadia Hassan, N., Adilin Mohd Abd Majid, H., Raju, R., & Nordin, R. (2018). Multilayer Neural Network Approach in Heart Disease Prediction: Investigating a Framework. International Journal of Engineering & Technology, 7(4.42), 182-185. https://doi.org/10.14419/ijet.v7i4.42.25707