Automated Heart Syndrome Forecast Model Exploiting Machine Learning Approaches

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Published: Oct 7, 2023
DOI: https://doi.org/10.17762/ijritcc.v11i11s.8158
Keywords:
Heart Infection, Machine Learning Techniques, K-NN, Artificial Neural Network, Decision Trees

Main Article Content

Parisha
Research Scholar, Dept. of Computer Science and Engineering, Babu Banarsi Das University, Lucknow, India
Gaurav Kumar Srivastava
Assistant Professor, Dept. of Computer Science and Engineering, Babu Banarsi Das University, Lucknow, India
Santosh Kumar
Associate Professor, School of Computing Science & Engineering, Galgotias University, Greater Noida, India

Abstract

Heart disease is a frequent condition that appears as a result of a poor diet and an irregular lifestyle. It is one of the most frequent diseases worldwide, with numerous reasons that damage the heart and have claimed countless lives in recent years. Due to the enormous number of risk factors for heart disease, it is critical to adopt a precise and dependable approach to provide an early diagnosis and correct prognosis. As a result, there is a broad potential for implementing various types of machine learning approaches for retrieving such critical data from the database. This study evaluates numerous machine learning algorithms for correctly predicting cardiac sickness and offers analytical findings, with an emphasis on various methodologies.

Article Details

How to Cite
Parisha, P., Srivastava, G. K. ., & Kumar, S. . (2023). Automated Heart Syndrome Forecast Model Exploiting Machine Learning Approaches. International Journal on Recent and Innovation Trends in Computing and Communication, 11(11s), 319–324. https://doi.org/10.17762/ijritcc.v11i11s.8158
Issue
Vol. 11 No. 11s (2023)
Section
Articles

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