Heart Disease Detection and Prediction Using ML Algorithms in Python

DOI

10.2991/978-94-6463-252-1_38How to use a DOI?

Keywords

Heart disease; KNN; logistic regression; and probability

Abstract

Cases of cardiac disease are growing at a disturbing rate. It is critical to diagnose and anticipate any such disorders. This conclusion is a difficult task that must be completed unambiguously and successfully. The research efforts on which patients are prospective to have heart disease are based on numerous medicinal features and health state factors. Developed a method for detecting and predicting heart disease by analyzing a patient’s medical times past to govern if the patient would be diagnosed with heart disease. Machine learning approaches such as linear regression, logistic regression, and K-Nearest Neighbors to forecast and categorize a patient with heart disease. A supportive method was utilized on the way to control how the model may be used to boost the precision of heart Risk in each single. The presented model’s ability hushed up fulfilling and could forecast proof of taking heart disease in a certain separate by applying KNN and logistic regression, which demonstrated great precision in contrast to previously utilized classifiers such as naive bays. As a result, by using the offered technique in determining the probability of the classifier properly and precisely recognizing a heart condition, a crucial measure of stress has been relieved. The Provided heart disease prediction system enhances clinical consideration while lowering costs. This research provides important information that can assist us anticipate patients with heart disease, and this one is written in Python.

Copyright

© 2023 The Author(s)

Open Access

Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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TY  - CONF
AU  - V. Ramesh
AU  - M. Swamy Das
AU  - B. Nageshwar Rao
PY  - 2023
DA  - 2023/11/09
TI  - Heart Disease Detection and Prediction Using ML Algorithms in Python
BT  - Proceedings of the Second International Conference on Emerging Trends in Engineering (ICETE 2023)
PB  - Atlantis Press
SP  - 347
EP  - 354
SN  - 2352-5401
UR  - https://doi.org/10.2991/978-94-6463-252-1_38
DO  - 10.2991/978-94-6463-252-1_38
ID  - 2023
ER  -