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The rationale of conducting this research may be rooted in the fact that with existing resources to deliver asthma prediction, there are some shortcomings regarding their stability and accuracy. However, problems like increased model complexity and difficulties in both the interpretation and translation of the models into clinical practice remain, which are quite rigid. Therefore, the purpose of this research is to develop a more accurate model with the help of voting algorithms such as Random Forest and Extra Trees while using the ensemble model. The goal for developing the model is to enhance the ability of predicting the health outcomes of asthma with the intention of assisting in the enhancement of the delivery of services to the patient. The results obtained here show that this works at an accuracy of 97% which suggests that this approach to the management of Asthma is viable as suggested above despite the difficulties that may be encountered.

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Voting Algorithms in Machine Learning: Enhancing Predictive Models for Asthma Management

  • 会议论文

  • 发表日期：

    2025年

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  • Abu Raihan Dider,
  • Sheshang Degadwala,
  • Dhairya Vyas

摘要

The rationale of conducting this research may be rooted in the fact that with existing resources to deliver asthma prediction, there are some shortcomings regarding their stability and accuracy. However, problems like increased model complexity and difficulties in both the interpretation and translation of the models into clinical practice remain, which are quite rigid. Therefore, the purpose of this research is to develop a more accurate model with the help of voting algorithms such as Random Forest and Extra Trees while using the ensemble model. The goal for developing the model is to enhance the ability of predicting the health outcomes of asthma with the intention of assisting in the enhancement of the delivery of services to the patient. The results obtained here show that this works at an accuracy of 97% which suggests that this approach to the management of Asthma is viable as suggested above despite the difficulties that may be encountered.