


Volume 20 No 22 (2022)
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Implementing Bio Inspired Algorithms for Prediction of Heart Disease
Dileep P, Revathy P
Abstract
Caused by both environmental and genetic factors, the prevalence of health problems is steadily rising. In this regard, heart disease has emerged as the leading killer of humans in recent decades. In this research, we employ an Artificial Neural Network trained with a swarm Intelligence approach to the problem of cardiac disease prediction. Swarm intelligence (SI) is a cutting-edge academic subfield that spans traditionally separate disciplines. Swarm-based algorithms are a new class of population-based algorithms that take their cues from nature and can efficiently solve several difficult problems with minimal resources. Group Search Optimization (GSO), Artificial Bee Colony (ABC), Ant Colony Optimization (ACO), and Particle Swarm Optimization (PSO), to name a few, are only a few of the swarm intelligence methods for optimization. This study argues that Particle Swarm Optimization (PSO) is the best performing and most popular Intelligence Algorithm for optimization problems. The purpose of this research is to use the Feed forward method of Artificial Neural Networks (ANNs) to distinguish between patients with and without cardiac disease. We have tested our novel categorization method on the industry-standard datasets.
Keywords
Caused by both environmental and genetic factors, the prevalence of health problems is steadily rising.
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