All Insights Report Predictive Horizons: Unveiling Cardiovascular Insights with Conditional Inference Trees

    Predictive Horizons: Unveiling Cardiovascular Insights with Conditional Inference Trees

    RWE, HEOR & Evidence Synthesis

    Predictive Horizons: Unveiling Cardiovascular Insights with Conditional Inference Trees

    This study employs machine learning on a Kaggle dataset to predict heart disease early, using the Near Miss algorithm and a Conditional Inference Tree model to enhance reliability and identify protective factors for improved patient outcomes.

    Download Report
    Predictive Horizons: Unveiling Cardiovascular Insights with Conditional Inference Trees

    This study uses machine learning to predict heart disease early, focusing on key risk factors to improve patient outcomes and reduce healthcare burdens. An open dataset from Kaggle, featuring data from the Behavioral Risk Factor Surveillance System (BRFSS), was used. To address the lower prevalence of heart disease, the Near Miss algorithm balanced the sample by under-sampling prevalent cases, enhancing model reliability. The study prioritizes identifying protective factors to inform preventive strategies. A Conditional Inference Tree (CIT) model was employed, with careful tuning and the Bonferroni correction ensuring statistical validity. This approach captures complex relationships and provides accurate heart disease predictions.

    This report is a poster presentation of Axtria from ISPOR 2024.

    Contact us at connect@axtria.com with any questions.

    Complete the brief form to download the white paper

    Recommended insights

    Predictive Horizons

    Report

    The Inflation Reduction Act and Its Implications on Real-World Evidence

    Predictive Horizons

    Report

    The Use of Natural Language Processing in Literature Reviews

    Predictive Horizons

    Report

    Novel Drug Approvals by the U.S. Food and Drug Administration in Rare Diseases: Findings From 2020–2023