A KNN and ANN model for predicting heart diseases
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Date
2024-07-03
Journal Title
Journal ISSN
Volume Title
Publisher
Explainable Artificial Intelligence in Medical Decision Support Systems
Abstract
The heart is the single most important organ in the human body. Patients, professions,
and medical systems are all bearing the brunt of heart failure’s devastating effects on
contemporary society. Since cardiac arrest may well be demonstrated as a better
understanding or conceivably go unobserved, particularly in the vast population of
clients that have other cardiovascular disorders, the true prevalence of heart failure is
likely to be underestimated, accounting for only 1–4% of all hospitalized patients as test
procedures in developed nations.A person with heart failure has a heart that is unable to
circulate sufficient blood through the body, but the term“heart failure” does not explain
why this happens. The clinical picture is confusing since there are several possible
causes of heart problems, many of which are diseases in and of themselves. Many cases
of heart failure can be avoided if the underlying medical conditions that cause them are
identified and treated promptly. The study and prediction of cardiac conditions must be
precise because numerous diseases have been connected to the cardiovascular system.
The resolution of this problem requires intensive online research on the relevant topic.
Since incorrect illness prognoses are a leading cause of death among heart patients,
learning more about effective prediction algorithms is crucial.
This research utilizes K-nearest neighbor (KNN) and artificial neural network
(ANN) to assess cardiovascular diseases using data collected from Kaggle. The
highest accuracy (96%) was achieved by ANN trained with the standard scalar.
Medical experts, specialists, and academics can all benefit greatly from this study.
Based on the results of this study, cardiologists will be able to make more knowledgeable
decisions about the inhibition, analysis, and handling of heart disease