A KNN and ANN model for predicting heart diseases
| dc.contributor.author | Abdulsalam, Sulaiman Olaniyi | |
| dc.contributor.author | Arowolo, Micheal Olaolu | |
| dc.contributor.author | Udofot, Enobong Chidera | |
| dc.contributor.author | Sanni, Ayodeji Matthew | |
| dc.contributor.author | Popoola, Damilola David | |
| dc.contributor.author | Adebiyi, Marion Olubunmi | |
| dc.date.accessioned | 2025-01-26T11:22:23Z | |
| dc.date.available | 2025-01-26T11:22:23Z | |
| dc.date.issued | 2024-07-03 | |
| dc.description.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 | |
| dc.identifier.other | https://doi.org/10.1049/PBHE050E_ch12 | |
| dc.identifier.uri | https://kwasuspace.kwasu.edu.ng/handle/123456789/3345 | |
| dc.publisher | Explainable Artificial Intelligence in Medical Decision Support Systems | |
| dc.title | A KNN and ANN model for predicting heart diseases |