Year: 2025
Type: article
Abstract: Heart ailment is one of the most acknowledged and lethal illnesses within the world, and many human beings lose their lives from this disease every year. Early detection of this disease is essential to store people’s lives. Machine Learning (ML), an synthetic intelligence technology, is one of the most handy, quickest, and low-cost ways to discover sickness. In this look at, we purpose to attain an ML model that can expect heart sickness with the highest possible performance the use of the Cleveland coronary heart disease dataset. The functions in the dataset used to train the version and the selection of the ML algorithm have a significant effect at the performance of the model. To keep away from overfitting (because of the curse of dimensionality) due to the huge quantity of functions inside the Cleveland dataset, the dataset became decreased to a lower dimensional subspace the usage of the Jellyfish optimization set of rules. The Jellyfish algorithm has a excessive convergence speed and is flexible to locate the exceptional functions.
Authors: Utkarsh Varshney -, Mohammad Haris -, Tanay Sahu -
Institution: Galgotias University
Language: English
Subfield: Health Information Management
Field: Health Professions
Domain: Health Sciences
Open Access status: hybrid
OpenAlex ID: W4407173900