Work

Heart Disease Predication Using Machine Learning

Year: 2025

Type: article

Abstract: Heart-related diseases or Cardiovascular Diseases (CVDs) have been the leading cause of death globally. There is a need for a reliable and accurate system to diagnose them early. This research explores machine learning algorithms such as Random Forest, SVM, and hybrid models to predict heart disease using the UCI dataset. The system uses key medical parameters like cholesterol, heart rate, and chest pain type. Among the tested models, Random Forest achieved the best performance with high accuracy and AUC score, offering a promising tool for early diagnosis.

Source: Zenodo (CERN European Organization for Nuclear Research)

DOI: 10.5281/zenodo.15307129

Authors: Sharma, Dev, Gond, Animesh, Kumar, Rohit, Mishra, Vanshdeep

Language: English

Topic: Artificial Intelligence in Healthcare

Subfield: Health Information Management

Field: Health Professions

Domain: Health Sciences

SDG: Good health and well-being

Open Access status: green

OpenAlex ID: W6949267104

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