International Journal For Multidisciplinary Research

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Heart Disease Prediction Using Machine Learning

Author(s) Harsh Gupta, Sandeep Kumar, Ankit Jangra, Kulveer Gahlaut, Chadive Indrasena Reddy, Priya Mankotia
Country India
Abstract The heart plays an important part in a living creature. The opinion and prognosis of a heart complaint must be made easily, exhaustively, and directly, because the slightest negligence can lead to serious complications or death. Numerous heart conditions are risk factors for death, and the number is gradually increasing. To solve this problem, prophetic styles that will ameliorate people's understanding of the complaint are urgently demanded. Machine literacy is a part of AI known for providing predictive support for any situation that requires training from natural wonders. In this, we compute the fineness of ML algorithms for cardiac prognostication, similar to k-nearest neighbor, decision tree, direct retrogression, and support vector machines, through training and evaluation using the UCI repository dataset (SVM). Anaconda (Jupytor) Primer is the stylish tool to use Python programming. It has colorful functions in the library and title lines to make it more effective and accessible.
Keywords Anaconda, Decision Tree
Field Engineering
Published In Volume 5, Issue 6, November-December 2023
Published On 2023-11-25
Cite This Heart Disease Prediction Using Machine Learning - Harsh Gupta, Sandeep Kumar, Ankit Jangra, Kulveer Gahlaut, Chadive Indrasena Reddy, Priya Mankotia - IJFMR Volume 5, Issue 6, November-December 2023. DOI 10.36948/ijfmr.2023.v05i06.9324
DOI https://doi.org/10.36948/ijfmr.2023.v05i06.9324
Short DOI https://doi.org/gs63wd
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