Metaheuristic Methods for Efficiently Predicting and Classifying Real Life Heart Disease Data Using Machine Learning

Elia Ramirez-Asis, Magna Guzman-Avalos, Bireshwar Dass Mazumdar, D Lakshmi Padmaja, Manmohan Mishra, Deepali S Hirolikar, Karthikeyan Kaliyaperumal and Vijay Kumar

Mathematical Problems in Engineering, 2022, vol. 2022, 1-5

Abstract: The heart attack happens if the flow of blood leads to blocks in any of the blood veins and vessels liable for delivering blood into internal parts of the heart. In the modern life activities and habits, the males and females hold the same responsibility and burden of risk. The absence of understanding frequently leads to a postponement in dealing with the heart attack issues, which could worsen the injury and in most of the situations shown to be dead. Several researchers have applied data mining techniques to diagnose illnesses, and the results have been encouraging. Some methods forecast a specific illness, whereas others predict a wide spectrum of illnesses. In addition, the accuracy of sickness predictions can be improved. This post went into great length on the many approaches of data classification that are currently available. Algorithms primarily represent themselves through representations. Data classification is a typical but computationally intensive task in the area of information technology. A huge amount of data must be analysed in order to come up with an effective plan for fighting disease. Metaheuristics are frequently employed to tackle optimization issues. The accuracy of computing models can be improved by using metaheuristic techniques. Early disease diagnosis, severity evaluation, and prediction are all popular uses for artificial intelligence. For the sake of patients, health care costs, and slowed course of disease, this is a good idea. Machine learning approaches have been used to achieve this. Using machine learning and metaheuristics, this study attempts to classify and forecast human heart disease.

Date: 2022
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Persistent link: https://EconPapers.repec.org/RePEc:hin:jnlmpe:4824323

DOI: 10.1155/2022/4824323

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