CORONARY HEART DISEASE CLASSIFICATION USING IMPROVED PENGUIN EMPEROR OPTIMIZATION-BASED LONG SHORT TERM MEMORY NETWORK

Rajeshwari Maramgere Ramaiah; Kavitha Kuntaegowdanalli Srikantegowda

doi:10.31436/iiumej.v24i2.2698

Authors

Rajeshwari Maramgere Ramaiah Visvesvaraya Technological University https://orcid.org/0000-0002-0618-4539
Kavitha Kuntaegowdanalli Srikantegowda Dayananda Sagar college of Engineering https://orcid.org/0000-0001-7342-6967

DOI:

https://doi.org/10.31436/iiumej.v24i2.2698

Keywords:

Coronary Heart Disease, Fuzzy C-Means and Enhanced Fuzzy Rough Set method, Improved Penguin Emperor Optimization, Ventricular Fibrillation

Abstract

Ventricular fibrillation (VF) is the most life-threatening and dangerous type of Cardiac Arrhythmia (CA), with a mortality rate of 10-15% in a year. Therefore, early detection of cardiac arrhythmia is important to reduce the mortality rate. Many machine learning algorithms have been proposed and have proven their usefulness in the classification and detection of heart problems. In this research manuscript, a novel Long Short Term Memory (LSTM) classifier with Improved Penguin Optimization (IPEO) is implemented for VF classification. The IPEO is used in finding optimal hyperparameters that overcome the overfitting problem. The presented model is tested, trained, and validated using two standard datasets that are available publicly: Massachusetts Institute of Technology-Beth Israel Hospital (MIT-BIH) and the China Physiological Signal Challenge (CPSC) 2018 dataset. Both of them consist of ECG recordings for five seconds of coronary heart disease (CHD) patients. Furthermore, Fuzzy C-Means and Enhanced Fuzzy Rough Set method (FCM-ETIFRST) are used for feature selection to extract informative features and to cluster membership degree, non-membership degree, and hesitancy degree. On the MIT-BIH dataset, the proposed model achieved accuracy, sensitivity, specificity, precision, and Matthews’s correlation coefficient (MCC) of 99.75%, 98.29%, 98.39%, 98.35%, and 97.79% respectively. On the CPSC 2018 dataset, the proposed model achieved accuracy of 99.79%, sensitivity of 99.11%, specificity of 98.20%, precision of 99.43%, and MCC of 98.57%. Hence, the results proved that the proposed method provides better results in the classification of VF.

ABSTRAK: Pemfibrilan Ventrikel (VF) adalah ancaman nyawa nombor satu dan jenis Aritmia Jantung (CA) berbahaya dengan kadar kematian 10-15% setahun. Oleh itu, pengesanan awal Aritmia Jantung sangat penting bagi mengurangkan kadar kematian. Terdapat banyak algoritma pembelajaran mesin yang telah dicadangkan dan terbukti berkesan dalam pengelasan dan pengesanan sakit jantung. Kajian ini mencadangkan kaedah baru pengelasan Memori Ingatan Jangka Panjang Pendek (LSTM) dengan Pengoptimuman Penambahbaikan Penguin (IPEO) yang dilaksanakan bagi klasifikasi VF. IPEO digunakan bagi mencari hiperparameter yang dapat mengatasi masalah padanan berlebihan. Model yang dicadangkan diuji, dilatih dan disahkan menggunakan dua dataset piawai yang dapat diperoleh secara terbuka: Institut Teknologi Hospital Massachusetts-Beth Israel (MIT-BIH) dan Cabaran Signal Psikologi Cina 2018 (CPSC). Kedua-dua data ini mempunyai rakaman ECG selama lima saat daripada pesakit Penyakit Jantung Koronari (CHD). Malah, kajian itu turut menggunakan Purata-C Kabur dan Kaedah Set Kasar Kabur Dipertingkat (FCM-ETIFRST) untuk pemilihan bagi mengekstrak ciri-ciri dan mengelaskan kelompok tahap keahlian, bukan ahli dan tahap keraguan. Bagi dataset MIT-BIH, model yang dicadangkan mencapai ketepatan, tahap sensitif, tahap spesifik, kejituan dan pekali kaitan Matthews (MCC) sebanyak 99.75%, 98.29%, 98.39%, 98.35%, dan 97.79% masing-masing. Bagi dataset CPSC 2018 pula, model yang dicadangkan mencapai ketepatan sebanyak 99.79%, 99.11% tahap sensitif , 98.20% tahap spesifik, 99.43% kejituan dan 98.57% MCC. Oleh itu, dapatan kajian membuktikan kaedah yang dicadangkan menunjukkan keputusan lebih baik dalam pengelasan VF.

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