用决策树预测哮喘患病率的机器学习方法

Abeda Begum Mahammad, Rajeev Kumar
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引用次数: 0

摘要

深度学习和机器学习算法广泛用于医疗保健领域的诊断,许多算法已成功实施,以执行患者疾病诊断和治疗计划。决策树算法在医疗保健行业中被广泛用于实现各种疾病诊断、预测、治疗建议、自动化任务以及患者和客户服务之间的通信的方法。决策树与分类和回归技术一起有效地工作。决策树是一种易于快速有效地实现疾病诊断更快结果的方法,在数据挖掘和决策过程中得到了全面的应用。决策树与随机森林和梯度增强等集成方法相结合,提高了与回归任务相关的预测结果的性能和准确性。哮喘是一种影响全世界大量人口的炎症性慢性疾病,病情严重时需要紧急前往医院。哮喘是一种由气道炎症引起的肺部疾病,气道对过敏物质变得敏感。及时发现这种疾病可以避免不良事件和重症监护就诊,并且是患者康复良好预后的基础。预防措施可以通过在早期阶段了解疾病水平和相关并发症来减少疾病进展的并发症。这篇研究文章想把重点放在用决策树算法预测哮喘患病率的最佳模型上,因为这些技术工作更快,提供更快的报告。Weka工具用于使用从数据中下载的数据集创建模型。世界和加州公共卫生部的开放数据门户网站。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Machine Learning Approach to Predict Asthma Prevalence with Decision Trees
Deep Learning and Machine Learning algorithms are popularly used in the healthcare sector for diagnosis with many algorithms have been successfully implemented to perform patient disease diagnosis and treatment plans. Decision Tree algorithms are profoundly used in the healthcare industry to implement the methods for various disease diagnoses, predictions, therapeutic recommendations, automated tasks, and communication between patients and customer service. Decision Trees work effectively with classification as well as regression techniques. Decision Trees are easy and swift to efficiently implement for faster outcomes in disease diagnosis and they are comprehensively used in data mining and decision-making processes. Decision Trees conjoined with ensemble methods such as Random Forest and Gradient Boost, enhance the performance and accuracy of results in predictions associated with regression tasks. Asthma is an inflammatory and chronic disease that affects a large population worldwide, with severe conditions resulting in emergency visits to the hospital. Asthma is a lung disease caused by airway inflammation and the airways become sensitive to allergic substances. Timely detection of this disease wards off undesirable events, and critical care visits, and is the basis for a good prognosis for patient recovery. Precautionary measures possibly reduce the complications of disease progression by knowing the disease level and associated complications at an early stage. This research article wants to focus on the best model for predicting Asthma prevalence with Decision Tree algorithms as these techniques work faster and provide quicker reports. The Weka tool was used for the model creation with datasets downloaded from data.world and The California Department of Public Health's Open Data Portal.
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