Outperforming Optimised Neural Networks for Cardiac Disease Detection

Authors

  • Mahesh Kothuru Department of CSE, GITAM School of Technology, GITAM (Deemed to be University), Visakhapatnam, Andhra Pradesh, India-530045
  • N. Suresh Kumar Department of CSE, GITAM School of Technology, GITAM (Deemed to be University), Visakhapatnam, Andhra Pradesh, India-530045

Keywords:

Cardiovascular Diseases, Electrocardiogram (ECG), Internet of Things (IoT), Machine Learning (ML), Genetic Algorithm (GA), Optimised ML model (OML), Particle Swam Optimization (PSO)

Abstract

Nowadays, cardiovascular diseases are common, and according to WHO, more than 17.9 million casualties per year are caused due to these diseases. It is vital to both the patient and physician to detect, analyse and treat before too late. The vast advancement in machine learning technology has made a path for identifying and classifying the potential abnormalities in a patient’s heart within no time using Electrocardiogram (ECG) signals, enables the physician to treat effectively and, in turn reduces the mortality rate. The accuracy of the existing machine learning (ML) models largely depends on the hyperparameters. Present research work successfully developed an Optimised ML model (OML) with Genetic Algorithm, and Particle Swam Optimization to identify and classify the abnormalities. This trained OML model shared over the IoT device helps in the early prediction of diseases by the patient as well as the hospital management system and helps the doctors to take up the necessary treatment. The results shows that OML models outperforms over the existing Non optimized ML models (NOML) in terms of various performance metrices.

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Published

03.09.2023

How to Cite

Kothuru, M. ., & Kumar, N. S. . (2023). Outperforming Optimised Neural Networks for Cardiac Disease Detection. International Journal of Intelligent Systems and Applications in Engineering, 12(1s), 761–770. Retrieved from https://ijisae.org/index.php/IJISAE/article/view/3548

Issue

Vol. 12 No. 1s (2024)

Section

Research Article

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