Scholarlink

This paper proposes an innovative approach of chronic obstructive pulmonary disease by trying to adapt algorithms of machine learning. The chronic respiratory disease is indeed COPD, factoring progressive airflow limitation and exacerbations which leads to big burdens on healthcare systems throughout the world. The proposed system employs the random forest algorithm to forecast COPD exacerbations through the analysis of the multiple patient data like lung function tests such as FEV1 and FVC, PEFR, SpO2, and RR. According to these parameters, it will give predictive models capable of showing individualized risk assessments to allow for the early use of intervention strategies tailored according to the patient’s needs. This method enhances clinical decision-making and patients’ outcomes but also adds to the evolution of predictive and preventive healthcare models. With this current system tagged with the authority of machine learning, it represents the significant advance in COPD management hopefully better-quality care and better allocation of properties of health care.

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

Exploiting Random Forest Algorithm Toward Forecasting Chronic Obstructive Pulmonary Disease Exacerbations

  • 会议论文

  • 发表日期：

    2025年

查看全文
  • Ravi Kumar Suggala,
  • Pavan Kumar Vadrevu,
  • Sri Krishna Adusumalli,
  • D. V. Nagaraju,
  • Ratna Kanth Gudala,
  • Sasi Kumar Bunga

摘要

This paper proposes an innovative approach of chronic obstructive pulmonary disease by trying to adapt algorithms of machine learning. The chronic respiratory disease is indeed COPD, factoring progressive airflow limitation and exacerbations which leads to big burdens on healthcare systems throughout the world. The proposed system employs the random forest algorithm to forecast COPD exacerbations through the analysis of the multiple patient data like lung function tests such as FEV1 and FVC, PEFR, SpO2, and RR. According to these parameters, it will give predictive models capable of showing individualized risk assessments to allow for the early use of intervention strategies tailored according to the patient’s needs. This method enhances clinical decision-making and patients’ outcomes but also adds to the evolution of predictive and preventive healthcare models. With this current system tagged with the authority of machine learning, it represents the significant advance in COPD management hopefully better-quality care and better allocation of properties of health care.