Authors : Yogesh Kumar; Geet Kiran Kaur; Ranjit Singh

Volume/Issue : Volume 9 - 2024, Issue 7 - July

Google Scholar : https://tinyurl.com/ye2x57jk

Scribd : https://tinyurl.com/z3s82exp

DOI : https://doi.org/10.38124/ijisrt/IJISRT24JUL1871

Note : A published paper may take 4-5 working days from the publication date to appear in PlumX Metrics, Semantic Scholar, and ResearchGate.

Abstract : Heart infections are responsible, for deaths and nowthey are a major contributor to depression in many individuals. To prevent fatalities, regular monitoring and early identification of heart conditions can significantly reduce the number of deaths.Detecting heart disease has become a task in the analysis ofdata. While accurately predicting heart infections may pose challenges employing advanced machine learning techniques can make it easier. Studies have shown that machine learning methods can effectively predict heart disease enabling detection and assessment of its severity. This approach aims to lower mortality rates decrease the severity of the illness and facilitate diagnosis. The field of therapy is undergoing advancements through the integration of machine learning techniques leading to enhanced accuracy in interpreting analyses. These techniques play a role,in identifying indicators for predicting cardiac diseases with precision. The presentation is put together using categorization techniques, such, as Decision Tree (DT) K Nearest Neighbors(K NN) Random Forest (RF) and Support Vector Machine (SVM). The performance of these four algorithms is assessedfrom angles, including specificity, recall, accuracy and precision. While precision varies SVM appears to deliver the results in this approach for calculations, in many instances.

Keywords : Heart Disease, Machine Learning, Prediction, Supervised Learning, Unsupervised Learning, Deep Learning.

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