Year: 2025
Type: article
Abstract: Heart disease remains a leading cause of mortality worldwide. Early prediction and diagnosis are crucial for effective treatment and management. This study employs the Random Forest algorithm to predict heart disease using a dataset comprising various clinical features. The methodology includes data preprocessing, feature encoding, model training, and evaluation. The model's performance is assessed using accuracy metrics and a confusion matrix. The results demonstrate the efficacy of the Random Forest algorithm in predicting heart disease, highlighting its potential as a reliable tool in clinical decision support systems.
Authors: Soumya K S, Alias Itten
Institution: Government of Kerala
Language: English
Subfield: Health Information Management
Field: Health Professions
Domain: Health Sciences
Open Access status: diamond
OpenAlex ID: W4409217807