Modeling and Optimization of Signals Using Machine Learning Techniques

8Hybridization of Artificial Neural Network with Spotted Hyena Optimization (SHO) Algorithm for Heart Disease Detection

Shwetha N.^1*, Gangadhar N.¹, Mahesh B. Neelagar², Sangeetha N.¹ and Virupaxi Dalal³

¹Dr. Ambedkar Institution of Technology, Karnataka, India

²Department of VLSI Design and Embedded Systems, VTU, Belagavi, Karnataka, India

³Department of ECE, Jain College of Engineering and Research, Belagavi, India

Abstract

Heart-related illnesses are the leading cause of mortality globally, which causes a high number of deaths in poor- and middle-income nations like India. Large amounts of data are constantly being generated by medical professionals. The generated data can be used to diagnose heart disease in advance, which can efficiently diminish the incidence of various heart-related diseases. Predictions can be done effectively by improving the knowledge identification needed to detect previously unknown patterns. Effective predictions can be made and hidden patterns can be detected by accessing data and concerns collected from healthcare industries. In this work, machine learning technique is used on cardiac disease-related data to try to find out the potential for heart disease before suffering from serious problems. Therefore, an artificial neural network (ANN) is used to predict a coronary illness. Additionally, the spotted hyena optimization (SHO) algorithm is hybridized with ANN to update the weights in an ANN. The implementation is carried out on the MATLAB platform. ...